This document summarizes a research paper that proposes a deep learning-based single object tracker for night surveillance video. The tracker uses pre-trained convolutional neural networks coupled with fully connected layers. The network is trained online during tracking to model changes in the target object's appearance as it moves. The paper tests different hyperparameters for the online learning, including optimization algorithms, learning rates, and ratios of positive and negative training samples, to determine the optimal setup for nighttime tracking. It evaluates the tracker on 14 night surveillance videos and finds that using the Adam optimizer with a learning rate of 0.00075 and a 2:1 ratio of positive to negative samples achieves the best accuracy.
Human motion is fundamental to understanding behaviour. In spite of advancement on single image 3 Dimensional pose and estimation of shapes, current video-based state of the art methods unsuccessful to produce precise and motion of natural sequences due to inefficiency of ground-truth 3 Dimensional motion data for training. Recognition of Human action for programmed video surveillance applications is an interesting but forbidding task especially if the videos are captured in an unpleasant lighting environment. It is a Spatial-temporal feature-based correlation filter, for concurrent observation and identification of numerous human actions in a little-light environment. Estimated the presentation of a proposed filter with immense experimentation on night-time action datasets. Tentative results demonstrate the potency of the merging schemes for vigorous action recognition in a significantly low light environment.
Discovering Anomalies Based on Saliency Detection and Segmentation in Surveil...ijtsrd
This paper proposes extracting salient objects from motion fields. Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the clustered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. We present algorithms for recognizing activity in monocular video sequences, based on discriminative gradient Random Field. Surveillance videos capture the behavioral activities of the objects accessing the surveillance system. Some behavior is frequent sequence of events and some deviate from the known frequent sequences of events. These events are termed as anomalies and may be susceptible to criminal activities. In the past, work was based on discovering the known abnormal events. Here, the unknown abnormal activities are to be detected and alerted such that early actions are taken. K. Shankar | Dr. S. Srinivasan | Dr. T. S. Sivakumaran | K. Madhavi Priya"Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5871.pdf http://www.ijtsrd.com/engineering/computer-engineering/5871/discovering-anomalies-based-on-saliency-detection-and-segmentation-in-surveillance-system/k-shankar
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS FOR IRIS RECOGNITIONijaia
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.
Human motion is fundamental to understanding behaviour. In spite of advancement on single image 3 Dimensional pose and estimation of shapes, current video-based state of the art methods unsuccessful to produce precise and motion of natural sequences due to inefficiency of ground-truth 3 Dimensional motion data for training. Recognition of Human action for programmed video surveillance applications is an interesting but forbidding task especially if the videos are captured in an unpleasant lighting environment. It is a Spatial-temporal feature-based correlation filter, for concurrent observation and identification of numerous human actions in a little-light environment. Estimated the presentation of a proposed filter with immense experimentation on night-time action datasets. Tentative results demonstrate the potency of the merging schemes for vigorous action recognition in a significantly low light environment.
Discovering Anomalies Based on Saliency Detection and Segmentation in Surveil...ijtsrd
This paper proposes extracting salient objects from motion fields. Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the clustered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. We present algorithms for recognizing activity in monocular video sequences, based on discriminative gradient Random Field. Surveillance videos capture the behavioral activities of the objects accessing the surveillance system. Some behavior is frequent sequence of events and some deviate from the known frequent sequences of events. These events are termed as anomalies and may be susceptible to criminal activities. In the past, work was based on discovering the known abnormal events. Here, the unknown abnormal activities are to be detected and alerted such that early actions are taken. K. Shankar | Dr. S. Srinivasan | Dr. T. S. Sivakumaran | K. Madhavi Priya"Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5871.pdf http://www.ijtsrd.com/engineering/computer-engineering/5871/discovering-anomalies-based-on-saliency-detection-and-segmentation-in-surveillance-system/k-shankar
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS FOR IRIS RECOGNITIONijaia
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.
In our World of today, the quest to get rich at all cost without working for our money has led some of our youth into crimes such as robbery and kidnapping. As a result of this and by the sheer fact that vehicles are now very expensive to buy these days, there is a need for people to safeguard their vehicles against these hoodlums to avoid loss of their precious Assets to these rampaging criminals. Tracking is technology that is used by many companies and individuals to track a vehicle, an individual or an asset by using many ways like GPS that operates using satellites and ground-based stations or by using our approach which depends on the cellular mobile towers. Vehicle tracking system is a system that can be used in monitoring and locating a vehicle, avoid theft or recover a stolen vehicle, for monitoring of vehicle routes to ensure strict compliance to an already defined vehicle routes, monitor driver’s behavior, predict bus arrival as well as for fleet management. Internet of things has made it very possible to devices to inter communicate amongst themselves and exchange information, helping in acquiring and analyzing information faster that we used to know in the past and this has helped more especially in vehicle monitoring to ensure that vehicle owners feel safe about their investments without fearing about their loss. In this paper, we propose a vehicle monitoring system based on IOT technology, using 4G/LTE to get the get the coordinate, speed, and overall condition of the vehicle, process and send to a remote server to be analyzed and used in locating the vehicle and monitor its other configured parameters. This is realized using Raspberry pi, 4G/LTE, GPS, Accelerometer and other sensors with communicate amongst themselves to get the environmental parameters which is processed and sent to a remote server where it is analyzed and represented on a map to locate the vehicle and monitor the other set parameters. 4G/LTE provides fast internet connectivity with overcomes the usual delay usually experienced in sending the acquired signals to be processed. The True Vehicle position is represented using google geolocation service and the actual position triangulated in real-time.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Leading water utility company in USA was facing a challenge to improve pipeline inspection process to reduce human errors and manual inspection time.Pipeline Anomaly Detection automates the process of identification of defects in pipeline videos, by a camera which notes the observations and lastly it generates the report.
There has been over the past few years, a very increased popularity for yoga. A lot of literatures have been published that claim yoga to be beneficial in improving the overall lifestyle and health especially in rehabilitation, mental health and more. Considering the fast-paced lives that individuals live, people usually prefer to exercise or work-out from the comfort of their homes and with that a need for an instructor arises. Hence why, we have developed a self-assisted system which can be used to detect and classify yoga asanas, which is discussed in-depth in this paper. Especially now when the pandemic has taken over the world, it is not feasible to attend physical classes or have an instructor over. Using the technology of Computer Vision, a computer-assisted system such as the one discussed, comes in very handy. The technologies such as ml5.js, PoseNet and Neural Networks are made use for the human pose estimation and classification. The proposed system uses the above-mentioned technologies to take in a real-time video input and analyze the pose of an individual, and classifies the poses into yoga asanas. It also displays the name of the yoga asana that is detected along with the confidence score.
In our World of today, the quest to get rich at all cost without working for our money has led some of our youth into crimes such as robbery and kidnapping. As a result of this and by the sheer fact that vehicles are now very expensive to buy these days, there is a need for people to safeguard their vehicles against these hoodlums to avoid loss of their precious Assets to these rampaging criminals. Tracking is technology that is used by many companies and individuals to track a vehicle, an individual or an asset by using many ways like GPS that operates using satellites and ground-based stations or by using our approach which depends on the cellular mobile towers. Vehicle tracking system is a system that can be used in monitoring and locating a vehicle, avoid theft or recover a stolen vehicle, for monitoring of vehicle routes to ensure strict compliance to an already defined vehicle routes, monitor driver’s behavior, predict bus arrival as well as for fleet management. Internet of things has made it very possible to devices to inter communicate amongst themselves and exchange information, helping in acquiring and analyzing information faster that we used to know in the past and this has helped more especially in vehicle monitoring to ensure that vehicle owners feel safe about their investments without fearing about their loss. In this paper, we propose a vehicle monitoring system based on IOT technology, using 4G/LTE to get the get the coordinate, speed, and overall condition of the vehicle, process and send to a remote server to be analyzed and used in locating the vehicle and monitor its other configured parameters. This is realized using Raspberry pi, 4G/LTE, GPS, Accelerometer and other sensors with communicate amongst themselves to get the environmental parameters which is processed and sent to a remote server where it is analyzed and represented on a map to locate the vehicle and monitor the other set parameters. 4G/LTE provides fast internet connectivity with overcomes the usual delay usually experienced in sending the acquired signals to be processed. The True Vehicle position is represented using google geolocation service and the actual position triangulated in real-time.
VIDEO SEGMENTATION FOR MOVING OBJECT DETECTION USING LOCAL CHANGE & ENTROPY B...csandit
Motion detection and object segmentation are an important research area of image-video
processing and computer vision. The technique and mathematical modeling used to detect and
segment region of interest (ROI) objects comprise the algorithmic modules of various high-level
techniques in video analysis, object extraction, classification, and recognition. The detection of
moving object is significant in many tasks, such as video surveillance & moving object tracking.
The design of a video surveillance system is directed on involuntary identification of events of
interest, especially on tracking and on classification of moving objects. An entropy based realtime
adaptive non-parametric window thresholding algorithm for change detection is
anticipated in this research. Based on the approximation of the value of scatter of sections of
change in a difference image, a threshold of every image block is calculated discriminatively
using entropy structure, and then the global threshold is attained by averaging all thresholds for
image blocks of the frame. The block threshold is calculated contrarily for regions of change
and background. Investigational results show the proposed thresholding algorithm
accomplishes well for change detection with high efficiency.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Leading water utility company in USA was facing a challenge to improve pipeline inspection process to reduce human errors and manual inspection time.Pipeline Anomaly Detection automates the process of identification of defects in pipeline videos, by a camera which notes the observations and lastly it generates the report.
There has been over the past few years, a very increased popularity for yoga. A lot of literatures have been published that claim yoga to be beneficial in improving the overall lifestyle and health especially in rehabilitation, mental health and more. Considering the fast-paced lives that individuals live, people usually prefer to exercise or work-out from the comfort of their homes and with that a need for an instructor arises. Hence why, we have developed a self-assisted system which can be used to detect and classify yoga asanas, which is discussed in-depth in this paper. Especially now when the pandemic has taken over the world, it is not feasible to attend physical classes or have an instructor over. Using the technology of Computer Vision, a computer-assisted system such as the one discussed, comes in very handy. The technologies such as ml5.js, PoseNet and Neural Networks are made use for the human pose estimation and classification. The proposed system uses the above-mentioned technologies to take in a real-time video input and analyze the pose of an individual, and classifies the poses into yoga asanas. It also displays the name of the yoga asana that is detected along with the confidence score.
Threshold based filtering technique for efficient moving object detection and...eSAT Journals
Abstract Detection and tracking of moving objects are an important research area in a video surveillance application. Object tracking is
used in several applications such as video compression, surveillance, robot technology and so on. Recently many researches has
been developed for video object detection, however the object detection accuracy and background object detection in the video
frames are still poses demanding issues. In this paper, a novel framework called Threshold Filtered Video Object Detection and
Tracking (TFVODT) is designed for effective detection and tracking of moving objects. TFVODT framework initially takes video
file as input, and then video frames are segmented using Median Filter-based Enhanced Laplacian Thresholding for improving
the video quality by reducing mean square error. Next, Color Histogram-based Particle Filter is applied to the segmented objects
in TFVODT framework for video object tracking. The Color Histogram-based Particle Filter measures the likelihood function,
particle posterior and particle prior function based on the Bayes Sequential Estimation model for improving the object tracking
accuracy. Finally, the objects detection is performed with help of Improvisation of Enhanced Laplacian Threshold (IELT) to
enhance video object detection accuracy and to recognize background moving object detection. The proposed TFVODT
framework using video images obtained from Internet Archive 501(c) (3) for conducting experiment and comparison is made with
the existing object detection techniques. Experimental evaluation of TFVODT framework is done with the performance metrics
such as object segmentation accuracy, Peak Signal to Noise Ratio, object tracking accuracy, Mean Square Error and object
detection accuracy of moving video object frames. Experimental analysis shows that the TFVODT framework is able to improve
the video object detection accuracy by 18% and reduces the Peak Signal to Noise Ratio by 23 % when compared to the state-ofthe-
art works.
Keywords: Object segmentation, Object tracking, Object Detection, Enhanced Laplacian Thresholding, Median
Filter, Color Histogram-based Particle Filter
Satellite Image Classification with Deep Learning Surveyijtsrd
Satellite imagery is important for many applications including disaster response, law enforcement and environmental monitoring etc. These applications require the manual identification of objects in the imagery. Because the geographic area to be covered is very large and the analysts available to conduct the searches are few, thus an automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a part of broader family of machine learning methods that have shown promise for the automation of such tasks. It has achieved success in image understanding by means that of convolutional neural networks. The problem of object and facility recognition in satellite imagery is considered. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. Roshni Rajendran | Liji Samuel ""Satellite Image Classification with Deep Learning: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30031.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30031/satellite-image-classification-with-deep-learning-survey/roshni-rajendran
Real Time Object Detection System with YOLO and CNN Models: A ReviewSpringer
The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK
ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This
survey is all about YOLO and convolution neural networks (CNN) in the direction of real time object detection.
YOLO does generalized object representation more effectively without precision losses than other object
detection models. CNN architecture models have the ability to eliminate highlights and identify objects in any
given image. When implemented appropriately, CNN models can address issues like deformity diagnosis,
creating educational or instructive application, etc. This article reached at number of observations and
perspective findings through the analysis. Also it provides support for the focused visual information and
feature extraction in the financial and other industries, highlights the method of target detection and feature
selection, and briefly describes the development process of yolo algorithm
Smart surveillance systems play an important role in security today. The goal of security systems is to protect users against fires, car accidents, and
other forms of violence. The primary function of these systems is to offer security in residential areas. In today’s culture, protecting our homes is
critical. Surveillance, which ranges from private houses to large corporations, is critical in making us feel safe. There are numerous machine learning algorithms for home security systems; however, the deep learning convolutional neural network (CNN) technique outperforms the others. The
Keras, Tensorflow, Cv2, Glob, Imutils, and PIL libraries are used to train and assess the detection method. A web application is used to provide a
user-friendly environment. The flask web framework is used to construct it. The flash-mail, requests, and telegram application programming interface (API) apps are used in the alerting approach. The surveillance system tracks
abnormal activities and uses machine learning to determine if the scenario is normal or not based on the acquired image. After capturing the image, it is
compared with the existing dataset, and the model is trained using normal events. When there is an anomalous event, the model produces an output from which the mean distance for each frame is calculated.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
2. Int J Elec & Comp Eng ISSN: 2088-8708
Deep-learning based single object tracker for night surveillance (Zulaikha Kadim)
3577
in the video. There are many researches has been done on these two topics, however, most of them focuses
on the bright environment, with little emphasize on dark environment.
Object tracking for night surveillance is a very challenging task mainly due to low information
captured by the normal RGB cameras. The captured images have low brightness, low contrast and nearly no
distinguishable color information. It is worst if the object is small in size, caused by the far distance from
the camera [1, 2]. Although, most of the recent cameras are equipped with night vision technology to
improve image quality in low-light condition, yet, the image quality is still no match as compared to the day
time image. In some cases, thermal infrared camera is used for night surveillance [3, 4], but the cost of this
type of camera is relatively too costly. Hence, night surveillance is normally performed using day/night
CCTV camera with addition of IR filter and IR illuminator for better night vision.
These days, deep-learning study has become a center of attention among researchers in diverse
fields that include object detection, classification, facial and speech recognition, rehabilitation, machine
translation and etc. [5-11]. Deep-learning is a subfield of machine learning that was inspired by the human
brain’s structure called neuron, which can be adapted to learn complex relationship [5] and can be extended
to multi-layer networks for non-linear problems. There are many types of deep-learning architecture, i.e.
Convolutional Neural Network (CNN), Generative Advesarial Network (GAN), Recurrent Neural Networks
(RNN) and etc. Among all of them, CNN is the most widely used architecture, especially in computer vision
field for object detection, recognition and tracking. CNN architecture was devised by Yann LeCun in
1998 [7], where the feature extractor is also trained instead of hand-crafted. Figure 1 shows an example of
basic CNN structure [12] that consists of two convolutional layers, two pooling layers, one fully connected
layer and one output layer that defines the final classification according to the number of classes.
The convolutional layers in CNN acts as the detection filters to extract specific features or patterns that are
presence in the image. An addition of a new layer will increase the complexity, thus allows it to capture more
abstract features.
Figure 1. An example of basic CNN network [12]
Due to the CNN capability, this paper proposes a method of online tracking of object of interest for
night surveillance application through deep-learning approach. A network with 3 convolutional layers and
3 fully connected layers is used to model the object appearance as proposed in [13]. The fully-connected
layers will be updated online to cater the changes in target object appearance as it moves around the scene
under different lighting conditions. Various hyperparameters for online learning are experimented, which
include the selection of optimization algorithms, online learning rates and training sample ratio to find
the optimal tracker setup. The main contributions of this work are:
- Online target tracking framework for night surveillance video that utilizes deep-learning approach to
dynamically represent target appearance model.
- Research on the impact of optimal online learning hyperparameters for the best overall tracking accuracy.
The remainder of this paper is organized as follows: Section 2 discusses some related works on
visual object tracking. Section 3 describes the proposed method, followed by experimental results and
discussion in Section 4. Finally, Section 5 concludes all the research findings.
2. RELATED WORKS
This section will discuss general approach to visual object tracking, followed by specialized tracker
for night surveillance applications and the evolution of object tracking algorithm towards deep-learning
approach. A good object tracker is defined as an algorithm that is capable of providing accurate object
localization with consistent object’s tracking label across successive frames. Object tracking studies have
been an active research field for the past several decades, and have demonstrated good progress in different
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3576 - 3587
3578
scenarios and applications. Most of the tracking algorithms are based on tracking-by-detection paradigm,
whereby object of interest is detected in every frame, which will beused to update the tracking states of
the object. This approach is heavily dependent on the detection accuracy. Thus, an improvment in
the detection algorithm will lead to better tracking accuracy accordingly. Among others, some good tracking-
by-detection algorithms are presented in [14-20]. Some of these tracking approaches are able to function
wellunder good lighting conditions, however, their performance deteriorate as the environment becomes
darker such as in night surveillance application.
Previously, one of the common approaches to improve tracking performance for night surveillance
is by introducing preprocessing module to enhance image quality for the case of underexposed and low
contrast environments. Some examples of the preprocessing step are histogram equalization, histogram
specification and intensity mapping. Another approach is through analyzing the contrast level so that object
detection will be improved before tracking is performed. This is based on the assumption that the human
visual system is dependent on the neighbourhood spatial relation to its background. Huang et al. [1] used
contrast changes information between successive frames to improve object detection accuracy in the night
video application. Local contrast is computed by dividing the local standard deviation of image intensity with
local mean intensity. Then, the object is detected by thresholding the contrast change between the successive
frames. The computation is quite fast, but the local contrast information to indicate the presence of object
of interest might be misleading as the background information itself may contrain high local contrast.
On the other hand, the object might have almost similar appearance that produces low local contrast.
Later in [21], Huang et al. proposed motion prediction and spatial nearest neighbour data association to
further suppress the false detection. In [2], Wang et al. improve Huang’s CC model by introducing salient
contrast change (SCC), which involve two more steps; online learning and analyzing the detected object
trajectories. By applying a threshold on the contrast change output, it is more sensitive to slight changes in
the lighting level. Thus Nazib et al. [22] multiplyed Shahnon’s entropy estimation with their own contrast
estimation to produce illumination invariant representation. In [23], vehicles in night surveillance videos are
detected by computing HOG features as input to support vector machine (SVM) to classify the detected
object either as a vehicle or not, before Kalman filter is applied to track the vehicles.
Apart from previously mentioned approaches, there are also a few researches that has exploited
camera technology to increase the detection and tracking accuracy in night environment. In [24],
the researchers has used far-infrared cameras to obtain the foreground information through background
subtraction technique. In [25], the researchers has used a near infrared camera to detect pedestrians using
adaptive preprocessing technique for the night environment. Another research in [26] has used a fusion of
two different types of camera, which are light visible camera and FIR camera mounted on a car to detect
pedestrians during the day and night times. Even with the help from improved camera technology, the total
cost of the sytems has risen because of more complex sensing hardwares.
Deep learning has been popularized by the introduction of AlexNet in 2012, when it has won
ImageNet competition for image classification task [27]. Ever since, deep learning has been widely applied in
many applications, overshadowing the other traditional machine learning approaches such as SVM and
artificial neural network (ANN). In [28], CNN is used to detect human presence in night surveillance videos
as an input to object tracker. Their proposed network consists of five convolutional layers and 3 fully
connected layers. The input image is resized to 183x119 first, before histogram equalization is applied for
human detection task. The proposed method is closely related to human/background classification in night
scenes rather than tracking problem. Another early effort in applying CNN in object tracking is proposed in
[29], where an online tracking framework based on multi-domain representations is proposed. Its architecture
consists of multiple shared layers that they refer as domain independent layers, where only the classification
layer is defined as the domain-specific ones. The shared layers are trained using multiple annotated video
sequences offline, while classification layer is trained separately based on each domain. When a new
sequence or domain is given, a new classification layer will be constructed to compute the target score based
on the new input. Then, the fully-connected layers within the shared layers and the new classification layer
will be updated peridiocally so that it is adapted to the new domain. In [30], multiple CNNs in TCNN is
maintained in a tree structure to represent multi-modal target appearance. It will update the CNN models in
the branches which has most similar appearance with the current target estimation. In [3, 13], a general
tracking framework for thermal infrared videos has been proposed. Thermal images exhibit similar properties
to night surveillance images where the target object usually consists of low contrast information and
negligable textures. In [3], multiple models are maintained to represent the target appearance in different
cases such as for the case of temporary occlusion. During network updates, parent nodes will be replaced by
the new node so that there is no redundancy in the pool of target object appearance models. In [13],
a Siamese approach is utilized in which pair of patches are compared to find the most likely location of
the target object in the current frame.
4. Int J Elec & Comp Eng ISSN: 2088-8708
Deep-learning based single object tracker for night surveillance (Zulaikha Kadim)
3579
3. METHODOLOGY
3.1. Tracker workflow
Figure 2 illustrates the overall workflow of the proposed tracking methodology. In the first frame,
the tracker is initialized using a single ground truth bounding box that encloses the object. Positive and
negative candidates are then generated using the given bounding box. Positive samples correspond to
the patches or subimages that represent the object of interest, while negative samples correspond to
subimages that belongs to the background. Let nt and mt be the number of positive and negative training
samples, respectively. Positive training data are generated by randomly shifting the initial bounding box
within a small distance (the shifted patch should at least consists of 80% overlap area with respect to
the original bounding box) and negative samples are generated by randomly shifting the initial bounding box
such that they will have minimal overlap area (overlap area with atmost 10% with respect to the initial
bounding box). After generating all the training samples, appearance features will be extracted using
the CNN networks to produce a feature vector with length of 512. Both sets of positive and negative feature
vectors are then used to train the rest of the fully connected layers, which will result in the trained model.
During online tracking, the process starts by generating the possible candidate samples location
pivoted on the last known location of the object. Total number of samples extracted is lesser compared to
training samples to speed up the tracking process. The features are then extracted and tested using the trained
network. The network output are the probabilities that the patch belongs to foreground object and background
data. The locations of n highest foreground probalities samples will then be used to update estimated location
of the tracked object in current input frame. Finally, the network is retrained or updated periodically to
capture the changes in object’s appearance as it moves around the scenes under different lighting exposure
and background.
Figure 2. Overall tracking flow
3.2. Network architecture
The network architecture consists of three convolutional layers and three fully connected layers
(FC). The first three convolutional layers weights and biases are obtained from VGG-M [31], which has been
trained on ImageNet dataset [32]. VGG-M is an eight layers network where the first five layers are
the convolutional layers, which function as feature extractor and the last three layers are the dense FC layers.
The original input size of VGG-M is 224x224. However, the proposed network uses only the first three
convolutional layers with input size of 75x75. Thus, all training and testing samples are resized to match
the corresponding input size. The full network architecture used in this work is illustrated in Figure 3.
The first CNN layer consists of 96 filters of 7x7 kernel. The stride step is 2 in x and y directions,
followed by ReLU activation function, local response normalization and 3x3 maximum pooling to produce
feature maps of size 17x17x96. The second convolution layer consists of 256 different filters of kernel size
5x5, which is then followed by ReLU activation function, local response normalization and 3x3 maximum
pooling to produce 3x3x256 feature maps. Finally, the third layer consists of 512 filters of kernel size 3x3,
which will produce feature maps of 1x1x512.
Both positive and negative extracted feature vectors are then used to train the three FC layers.
Final output from the last softmax layer are the two probabilities that represent the likelihood of the input
image patch belongs to the tracked object and the likelihood that the input image patch belongs to
the background. Initially, all FC parameters are randomly initialized. In this work, three different
optimization algorithms are experimented to train the FC layers: Gradient Descent [33], Adam [34] and
Adagrad [35] with four different learning rates: 0.00125, 0.001, 0.00075 and 0.0005.
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3576 - 3587
3580
Figure 3. Network architecture of the proposed tracking algorithm
3.3. Network learning parameters
In this work, only the last three FC layers will undergo retraining so that the network is adapted to
the changes in the object appearance. In the first frame, the weights of these layers are randomly initialized,
while the biases are fixed to 0.05. Learning parameter values for positive samples, negative samples, initial
learning rate and number of epoch are set to 500, 1000, 0.0005 and 150 respectively. Cross entropy (1) loss
function is used to train the network, where p is the true label, q is the predicted probability and x is
the number of output class. Since the network outputs are a set of two probabilities; probability that
the sample is foreground and background, thus x value is two where the summation of each sample
probabilities is equal to 1. Now, let the true label be 𝑝 𝑥=0 = 𝑦 and 𝑝 𝑥=1 = 1 − 𝑦, and the predicted
probability be 𝑞 𝑥=0 = 𝑦̂ and 𝑞 𝑥=1 = (1 − 𝑦̂). The loss function is then computed by taking the average cross
entropy of all N input samples (3).
Cross entropy,
𝐻(𝑝, 𝑞) = − ∑ 𝑝 𝑥 log 𝑞 𝑥𝑥 (1)
𝐻(𝑝, 𝑞) = −𝑦 log 𝑦̂ − (1 − 𝑦)log(1 − 𝑦̂) (2)
Loss function,
𝐽(𝑤) =
1
𝑁
∑ 𝐻(𝑝𝑖, 𝑞𝑖) =𝑁
𝑖=1 −
1
𝑁
∑ [𝑦𝑖 log 𝑦𝑖̂ + (1 − 𝑦𝑖) log(1 − 𝑦̂𝑖)]𝑁
𝑖=1 (3)
During online learning, number of training epoch is reduced to 75, while the other two parameters;
learning rate and number of positive and negative samples varies according to the best setup. Three different
optimizers; stochastic gradient descent, Adagrad and Adam (adaptive moment estimation) are compared to
find the optimal values of the model parameters (weights and biases) by minimizing the loss function.
3.3.1. Optimizer #1: Stochastic gradient descent (SGD)
Gradient descent [33] is a popular optimization technique and it has been widely used in network
learning [28, 29, 36]. At a time step t, gradient descent algorithm computes the gradient of loss function with
respect to the model parameters, where the resultant value is used to update the network. Gradient is a vector
of partial derivative of the loss function with respect to every weight and bias for the training samples.
Then, each of the weight and bias are updated by subtracting previous value with the multiplication of
the learning rate with the calculated gradient (5), (6). The process will be repeated until the loss function is
minimized (converge) or the maximum number of epoch is reached. One iteration of a gradient descent on
6. Int J Elec & Comp Eng ISSN: 2088-8708
Deep-learning based single object tracker for night surveillance (Zulaikha Kadim)
3581
one parameter is summarized as follows. Gradient of loss function with respect to parameter i for time step t
is calculated as:
𝑔𝑖,𝑡 =
1
𝑁
∑
𝜕𝐽
𝜕𝑤 𝑖,𝑡,𝑗
𝑁
𝑗=1 (4)
where N is the number of training samples.Then the weight and bias of parameter i for time step t is
calculated as in gradient step below:
𝑤𝑖,𝑡 = 𝑤𝑖,𝑡−1 − 𝜂𝑔𝑖,𝑡 (5)
𝑏𝑖,𝑡 = 𝑏𝑖,𝑡−1 − 𝜂𝑔𝑖,𝑡 (6)
where 𝜂 is the learning rate. Note that the same learning rate is applied to all parameters updates.
One gradient descent operation consists of one iteratation over all training samples. This is different
from stochastic gradient descent, whereby instead of taking the whole training samples, it randomly selects
a few training samples in each iteration to optimize the model parameters. This makes SGD computationally
effective and makes it popular for online network training. Nevertheless, since SGD uses only a few training
samples, the path to convergence will be noisy.
3.3.2. Optimizer #2: Adam (adaptive moment estimation)
Adam [34] optimizer stands for adaptive moment estimation. It computes different learning rate for
different parameters by using the estimates of first and second order moments of gradient. The first and
second order moments are the moving average and uncentered moving variance as shown in (4) and (5).
It introduces three more hyperparameters compared to gradient step in SGD, which are β1, β2 and ε; which
correspond to exponential decay rate for first order moment, exponentially decay rate for second order
moment and very small constant to prevent the case zero division, respectively. 1st
order moment (moving
average) of parameter i for time step t,
𝑚𝑖,𝑡 = 𝛽1 ∗ 𝑚𝑖,𝑡−1 + (1 − 𝛽1) ∗ 𝑔𝑖,𝑡 (7)
2nd
order moment (uncentered variance) of parameter i for time step t,
𝑣𝑖,𝑡 = 𝛽2 ∗ 𝑣𝑖,𝑡−1 + (1 − 𝛽2) ∗ 𝑔𝑖,𝑡
2
(8)
Estimation of these moments will be bias-corrected before they are used to update the model
parameters. This step is important to ensure that the first and second order moments are not biased towards
zero as the initial values of 𝑚0 and 𝑣0 are set to zero. Bias-corrected first and second order moments are
calculated as below. Bias-corrected 1st
order moment of parameter i for time step t,
𝑚̂ 𝑖,𝑡 =
𝑚 𝑖,𝑡
(1−𝛽1
𝑡
)
(9)
Bias-corrected 2nd
order moment of parameter i for time step t,
𝑣̂𝑖,𝑡 =
𝑣 𝑖,𝑡
(1−𝛽2
𝑡
)
(10)
After estimating the moments, model parameter is updated as in (8). Note that the learning rate
is now multiplied by the ratio of first and second order moments of the gradients. η is the learning rate and 𝜀
is a very small number to prevent division by zero. Updated weight and biases of parameter i for time step t,
𝑤𝑖,𝑡 = 𝑤𝑖,𝑡−1 − 𝜂
𝑚 𝑖,𝑡̂
√𝑣𝑖,𝑡̂ +𝜀
(11)
𝑏𝑖,𝑡 = 𝑏𝑖,𝑡−1 − 𝜂
𝑚 𝑖,𝑡̂
√𝑣𝑖,𝑡̂ +𝜀
(12)
Since its first introduction in 2015, Adam optimizer has been widely used in network learning [37]. It has fast
convergence rate and thus practical for training a large model with large training samples.
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3576 - 3587
3582
3.3.3. Optimizer #3: Adagrad
AdaGrad [35] optimizer is a gradient-based learning algorithm, but it computes different learning
rates for different parameters. AdaGrad performs smal update on the parameters that are associated with
frequently occurring features, while it performs big update on the parameters that are associated with
infrequent occurring features. This is achived by AdaGrad through modifying the general learning rate in (5),
based on the past gradient of the parameter i. The gradient step in AdaGrad becomes:
𝑤𝑖,𝑡 = 𝑤𝑖,𝑡−1 −
𝜂
√ 𝐺 𝑖,𝑡+𝜀
𝑔𝑖,𝑡 (13)
where 𝐺𝑖,𝑡 is the accumulated sum of the squares of the previous gradient with respect to the parameter i up to
time step t.
𝐺𝑖,𝑡 = ∑ 𝑔𝑖,𝑗
2𝑡
𝑗=1 (14)
Note that since the gradient values are all positive, the accumulated sum 𝐺𝑖,𝑡 will keep increasing
during the training process which will cause the learning rate in (13) to shrink and eventually become
infinitesimally small. At this point, the optimizer is not able to learn any new knowledge. Despite of this
weakness, AdaGrad still performs better compared to the SGD as the learning rate is not manually fine-tuned.
AdaGrad has been used at Google [38] to train large neural networks to recognize cats in youTube videos.
It is also used in [39] to train GloVe word embeddings, as infrequent words require much larger updates
compared to the frequent ones.
3.3.4. Learning rate
Choosing a learning rate can be a difficult task. A too small learning rate leads to a slow
convergence, while a too large learning rate can hinder convergence and causes loss function to fluctuate or
even cause training divergence. In this work, learning rates of 0.00125, 0.001, 0.00075 and 0.0005 are
experimented to find an optimal setup.
3.4. Object location estimation
Given an input frame during online tracking, the system will estimate the object location by
analyzing the output probabilities from the network. The network outputs two probabilities; (1) probabilities
that the input sample belongs to the foreground object and (2) probabilities that the input sample belongs to
the background. The final object’s location is estimated by computing the weighted average of the top five
samples with the highest foreground probabilities whereby the weight is based on their probability values.
4. RESULTS AND DISCUSSION
For validation purpose, 14 night scene videos of size 352x288 has been collected. In each video,
the tracked object size is about 30x70 pixels and the total number of accumulated frames of all video is 3646.
The chosen videos contain the challenge of various lighting condition, occlusion and move-stop-move
problem. Snapshot of the three camera views of the videos are shown in Figure 4. The groundtruth is
generated manually by drawing the object bounding box in each frame by an expert in computer vision.
(a) (b) (c)
Figure 4. Three camera views for fourteen testing videos (a) Cm01, (b) Cam02, (c) Cam03
8. Int J Elec & Comp Eng ISSN: 2088-8708
Deep-learning based single object tracker for night surveillance (Zulaikha Kadim)
3583
4.1. Implementation details
The tracking code is implemented in Python with tensorflow library. Original location of the tracked
object is given in the form of bounding box ([x0, y0, width0, height0]). In the first frame, hyperparameters for
learning rate, number of epoch, number of positive sample and number of negative sample are initialized to
0.0005, 150, 500 and 1000 respectively. Samples extracted from first frame is the most important step as it is
the only known groundtruth by the tracker. Figure 5 shows examples of positive and negative samples
extracted in the first frame of three different test sequences. Then, the tracker will be updated online
periodically through weak supervision as the consequent frames groundtruth data is not known.
Figure 5. Examples of positive and negative samples that has been extracted from the current frame
(first 20 samples), which are represented by blue and red boxes, respectively
4.2. Performance metric
To evaluate the performance of our night tracker algorithm, we use one of the VOT evaluation
metrics, which is accuracy (Ac) as defined in (4). Accuracy measures how well the tracked bounding box
relative to ground truth box by computing the intersection over union (IoU) area. A higher overlap area
represents a better tracking accuracy. The tracker is not re-initialized in the event of track failure (where
the IoU is zero).
Accuracy, 𝐴𝑐 =
1
𝜑
∑
𝑠 𝑖,𝑜𝑢𝑡𝑝𝑢𝑡∩𝑠 𝑖,𝑔𝑡
𝑠 𝑖,𝑜𝑢𝑡𝑝𝑢𝑡∪𝑠 𝑖,𝑔𝑡
𝜑
𝑖=1 (4)
where 𝜑 denotes the number of frames in the test video, while 𝑠𝑖,𝑜𝑢𝑡𝑝𝑢𝑡 and 𝑠𝑖,𝑔𝑡 are the bounding boxes of
object in frame i from the tracker output and ground truth, respectively.
Table 1 shows the accuracy comparison between the three optimizer algorithms: SGD, Adam and
Adagrad. For a fair comparison, learning rate, number of positive sample and number of negative sample are
fixed to 0.001, 50 and 100, respectively. Default values for Adam’s hyperparameters β1, β2 and ε are set to
0.9, 0.999 and 1e-08
, respectively. In average, Adam optimizer produces the best accuracy as compared to
the other two optimizers, followed by AdaGrad. Adagrad performs significantly better in Cam01-video08
compared to the other two optimizers. While, it is noted that SGD performs the worst in most of the test
videos. This indicates that the performance of adaptive learning rate method is better compared to a fixed
value. As the number of iterations for each training is set to minimum, SGD may not be able to converge and
contributes to its bad performance. Some samples of frame with overlaid tracking output for Cam01-video08,
Cam02–video02 and Cam03-video02 are shown in Figure 6. Green, blue and magenta bounding
boxescorrespond to the output of SGD, Adam and AdaGrad optimizers, respectively. In Figure 6, the first
row images correspond to Cam01-video08, in which AdaGrad optimizer gives the highest accuracy. Initially,
all three optimizers produce good results as shown in frame #2, then eventually SGD optimizer model has
drifted to mix with the background (frame #27) followed by Adam optimizer (frame #71). The second row
shows the images for Cam02-video02 sequences, in which Adam gives the best accuracy while the others
give almost 0% accuracy (the bounding boxes are stucked at the background area as it contain more
textures compared to thetracked object). The third row images correspond to the output for Cam03-video02,
in which all three optimizers produce poor accuracy results. This might be caused by similiarity between
the foreground appearance and the background.
Table 2 shows the accuracy comparison between four different values of learning rate. In this
experiment, Adam optimizer has been chosen as the basis optimizer, while the number of positive and
negative samples are set to 50 and 100, respectively. In average, learning rate of 0.00075 gives the best
accuracy performance compared to the others, followed by 0.0005 learning rate. The results also indicate that
an increase in learning rate value, the average tracker accuracy will be lower.
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3576 - 3587
3584
Table 3 shows the accuracy comparison between four different combinations of total number of
positive and negative training samples used during online update. Adam optimizer with learning rate of
0.00075 will be the basic setup for the training samples comparison. In average, a combination of 50 and 100
for positive and negative traiing samples, respectively returns the best accuracy compared to other
combinations. Total number of negative samples are twice of the positive samples, such that it caters for
larger background area compared to concentrated foreground samples.
Table 1. Accuracy comparison between three optimizer algorithms: SGD, Adam and Adagrad, with online
learning parameters; learning rate, number of positive and negative samples are fixed to 0.001,
50 and 100 respectively
No. Datasets Number of frames
Accuracy
Learning rate= 0.001
# positive samples=50
# negative samples=100
Optimizer: Adam Optimizer: SGD Optimizer: Adagrad
1 Cam01 – video01 146 85.02 15.71 69.81
2 Cam01 – video02 184 64.82 44.92 45.81
3 Cam01 – video03 71 96.89 0.44 57.35
4 Cam01 – video04 22 91.46 14.85 74.28
5 Cam01 – video05 34 89.51 73.42 25.17
6 Cam01 – video06 150 88.96 74.95 81.64
7 Cam01 – video07 86 55.79 6.88 20.96
8 Cam01 – video08 125 59.26 21.53 94.89
9 Cam02 – video01 257 67.56 0.86 35.71
10 Cam02 – video02 1083 62.27 0 3.8
11 Cam03 – video01 344 95.83 89.66 79.93
12 Cam03 – video02 227 13.58 12.15 2.97
13 Cam03 – video03 317 62.96 55.78 74.03
14 Cam03 – video04 600 36.97 48.32 45.54
Average accuracy 69.35 32.82 50.85
frame #2 frame #27 frame #71
frame #3 frame #105 frame #246
frame #2 frame #100 frame #154
Figure 6. Sample of frames with overlaid tracking output for (a) Cam01-video08, (b) Cam03-video02
and (c) Cam02-video02. Boxes color: green (SGD), blue (Adam) and magenta (AdaGrad)
10. Int J Elec & Comp Eng ISSN: 2088-8708
Deep-learning based single object tracker for night surveillance (Zulaikha Kadim)
3585
Table 2. Accuracy comparison between four online learning rates: 0.00125, 0.001, 0.00075 and 0.0005,
with online learning parameters; optimer algorithm, number of positive and
negative samples fixed to Adam, 50 and 100 respectively
No. Datasets
Number of
frames
Accuracy
Optimizer: Adam
# positive samples=50
# negative samples=100
Learning rate=
0.00125
Learning rate=
0.001
Learning rate=
0.00075
Learning rate=
0.0005
1 Cam01 – video01 270 68.23 85.02 78.91 83.28
2 Cam01 – video02 448 62.82 64.82 65.59 70.56
3 Cam01 – video03 71 98.23 96.89 89.20 94.10
4 Cam01 – video04 128 87.66 91.46 94.06 88.76
5 Cam01 – video05 34 89.24 89.51 76.20 89.02
6 Cam01 – video06 224 95.19 88.96 93.76 97.77
7 Cam01 – video07 460 48.22 55.79 59.81 54.06
8 Cam01 – video08 125 27.53 59.26 91.29 95.54
9 Cam02 – video01 1137 45.26 67.56 76.11 60.43
10 Cam02 – video02 1083 66.10 62.27 88.16 76.16
11 Cam03 – video01 344 88.21 95.83 91.63 80.73
12 Cam03 – video02 700 11.76 13.58 7.18 35.29
13 Cam03 – video03 317 64.93 62.96 64.32 76.41
14 Cam03 – video04 689 36.28 36.97 43.04 16.91
Average accuracy 63.55 69.35 72.80 72.79
Table 3. Accuracy comparison between four different combination of positive and negative samples (50,100),
(50,50), (100,100) and (150,150), with online learning parameters; optimizer algorithm and
learning rate is fixed as Adam and 0.00075 respectively
No. Datasets Number of frames
Accuracy
Optimizer: Adam
Learning rate=0.00075
n= 100
p= 50
n= 50
p= 50
n= 100
p= 100
n= 150
p= 150
1 Cam01 – video01 270 78.91 12.84 74.49 83.84
2 Cam01 – video02 448 65.59 55.00 54.78 54.58
3 Cam01 – video03 71 89.20 96.19 93.97 96.40
4 Cam01 – video04 128 94.06 91.05 95.27 92.85
5 Cam01 – video05 34 76.20 77.65 92.40 75.07
6 Cam01 – video06 224 93.76 92.22 92.99 97.57
7 Cam01 – video07 460 59.81 64.52 49.81 10.37
8 Cam01 – video08 125 91.29 91.01 90.41 34.12
9 Cam02 – video01 1137 76.11 65.09 6.43 5.27
10 Cam02 – video02 1083 88.16 73.88 55.29 80.56
11 Cam03 – video01 344 91.63 93.92 85.35 93.74
12 Cam03 – video02 700 7.18 11.99 10.82 10.24
13 Cam03 – video03 317 64.32 67.63 69.17 75.61
14 Cam03 – video04 689 43.04 34.35 14.22 43.14
Average accuracy 72.80 66.24 63.24 60.95
5. CONCLUSION
In conclusion, the proposed tracking scheme is able to track object of interest in the night
surveillance videos. Adam optimizer shows superior accuracy performance as compared to SGD and
AdaGrad in most of the testing videos. The best learning rate is found to be 0.00075 that are achieved by
using sample training ratio of 2:1 between negative and positive samples. Hence, this tracker can be
implemented in the higher level application of night surveillance system.
ACKNOWLEDGEMENTS
This work was supported by the Nvidia Corporation through the Titan V Grant (KK-2019-005) and
Ministry of Education through FRGS/1/2019/ICT02/UKM/02/1.
REFERENCES
[1] K. Huang, L. Wang, and T. Tan, “Detecting and tracking distant objects at night based on human visual system,”
Asian Conference on Computer Vision, pp. 822–831, 2006.
11. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3576 - 3587
3586
[2] Liangsheng Wang, Kaiqi Huang, Yongzhen Huang and Tieniu Tan, "Object detection and tracking for night
surveillance based on salient contrast analysis," 16th IEEE International Conference on Image Processing (ICIP),
Cairo, pp. 1113-1116, 2009.
[3] M. A. Zulkifley and N. Trigoni, “Multiple-Model Fully Convolutional Neural Networks for Single Object Tracking
on Thermal Infrared Video,” IEEE Access, vol. 6, pp. 42790–42799, 2018.
[4] S. K. Sharma, R. Agrawal, S. Srivastava, and D. K. Singh, “Review of Human Detection Techniques in Night
Vision,” International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET),
pp. 2216–2220, 2017.
[5] C. Szegedy, A. Toshev, and D. Erhan, “Deep Neural Network for Object Detection,” Neural Inf. Process. Syst.,
vol. 7, no. 5, pp. 200–205, 2013.
[6] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region
Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2017.
[7] Yann LeCun, Patrick Haffner, Léon Bottou, and Yoshua Bengio, “Object Recognition with Gradient-Based
Learning,” In Shape, Contour and Grouping in Computer Vision, pp. 319-.345, 1999.
[8] H. Wang, Y. Cai, X. Chen, and L. Chen, “Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning,”
Journal of Sensors, vol. 4, pp. 1-8, 2016.
[9] J. Gu, et al., “Recent advances in convolutional neural networks,” Pattern Recognition., vol. 77, pp. 354–377,
2018.
[10] M. A. Zulkifley, S. R. Abdani and N. H. Zulkifley, “Pterygium-Net: a deep learning approach to pterygium
detection and localization,” Multimedia Tools and Applications, vol. 78, no. 24, pp. 34563–34584, 2019.
[11] M. A. Zulkifley, S. R. Abdani and N. H. Zulkifley, “Squat angle assessment through tracking body movements,”
IEEE Access, vol. 7, pp. 48635-32392, 2019.
[12] Matthew Stewart, “Simple Introduction to Convolutional Neural Networks,” Towards Data Science, 2018, Online.
[Available]: https://towardsdatascience.com/simple-introduction-to-convolutional-neural-networks-cdf8d3077bac,
[Accessed May. 23, 2019].
[13] M. A. Zulkifley, “Two Streams Multiple-Model Object Tracker for Thermal Infrared Video,” IEEE Access, vol. 7,
pp. 32383-32392, 2019.
[14] Özuysal M., Lepetit V., Fleuret F. and Fua P., “Feature Harvesting for Tracking-by-Detection”, European
Conference on computer vision, pp. 592-605, 2006.
[15] M. Andriluka, S. Roth and B. Schiele, “People-tracking-by-detection and people-detection-by-tracking,” IEEE
Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2018.
[16] Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., and Schiele, B., “Learning People Detectors for
Tracking in Crowded Scenes,” IEEE International Conference on Computer Vision, vol. 1049-1056, 2013.
[17] Y. Xiang, A. Alahi and S. Savarese, "Learning to Track: Online Multi-object Tracking by Decision Making," 2015
IEEE International Conference on Computer Vision (ICCV), Santiago, pp. 4705-4713, 2015.
[18] Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman, “Detect to Track and Track to Detect,” IEEE International
Conference on Computer Vision (ICCV), pp. 3057-3065, 2017.
[19] M. A. Zulkifley, D. Rawlinson and B. Moran, “Robust observation detection for single object tracking:
Deterministic and probabilistic path-based approaches,” Sensors, vol. 12, no. 11, pp. 15638-15670, 2012.
[20] M. A. Zulkifley, “Robust single object tracker based on kernelled patch of a fixed RGB camera,” Optik-
International Journal of Light and Electron Optics, vol. 127, no. 3, pp. 1100-1110, 2016.
[21] Kaiqi Huang, Liangsheng Wang, Tieniu Tan, Steve Maybank, “A real-time object detecting and tracking system for
outdoor night surveillance,” Pattern Recognition, vol. 41, no. 1, pp. 432-444, 2008.
[22] Nazib, Abdullah, Oh, Chi-min and Lee Chil-Woo, “Object Detection and Tracking in Night Time Video
Surveillance,” 10th International Conference on Ubiquitous Robot and Ambient Intelligence, At Jeju Island, 2013.
Doi: 10.1109/URAI.2013.6677410
[23] Qing Tian, Long Zhang, Yun Wei, Wenhua Zhao, Weiwei Fei, “Vehicle Detection and Tracking at Night in Video
Surveillance,” International Journal of Online and Biomedical Engineering (iJOE), vol. 9, pp. 60-64, 2013.
[24] E. S. Jeon, et al., “Human Detection Based on the Generation of a Background Image by Using a Far-Infrared Light
Camera,” Sensors (Basel), pp. 6763–6788, 2015.
[25] T. Y. Han and B. C. Song, “Night Vision Pedestrian Detection based on Adaptive Preprocessing using Near
Infrared Camera,” IEEE International Conference on Consumer Electronics Asia (ICCE-Asia), pp. 9–11, 2016.
[26] A. González, et al., “Pedestrian Detection at Day/Night Time with Visible and FIR Cameras : A Comparison,”
Sensors (Basel), pp. 1–11, 2016.
[27] Krizhevsky, Alex et al. “ImageNet Classification with Deep Convolutional Neural Networks,” Communication
ACM, vol. 60, no. 6, pp. 84-90, 2012.
[28] Hyun Kim, Jong, Hong, Hyung and Ryoung Park, Kang, “Convolutional Neural Network-Based Human Detection
in Nighttime Images Using Visible Light Camera Sensors,” Sensors, vol. 17, no. 5, 2017.
[29] H. Nam and B. Han, “Learning multi-domain convolutional neural networks for visual tracking,” Proc. IEEE Conf.
Computer Vision Pattern Recognition, pp. 4293–4302, Jun. 2016.
[30] H. Nam, M. Baek, and B. Han, “Modeling and propagating CNNs in a tree structure for visual tracking,” Aug.
2016, [Online], Available: https://arxiv.org/abs/1608.07242, [Accessed May 31, 2019].
[31] Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, “Return of the Devil in the Details:
Delving Deep into Convolutional Networks,” BMVC, 2014.
12. Int J Elec & Comp Eng ISSN: 2088-8708
Deep-learning based single object tracker for night surveillance (Zulaikha Kadim)
3587
[32] O. Russakovsky, et al., “ImageNet large scale visual recognition challenge,” International J. Computer Vision,
vol. 115, no. 3, pp. 211–252, Dec. 2015.
[33] Sebastian Ruder, “An overview of gradient descent optimisation algorithms,” arXiv preprint arXiv:1609.04747,
2017.
[34] Diederik P. Kingma and Jimmy Lei Ba Adam, “A method for stochastic optimization,” arXiv:1412.6980v9, 2014.
[35] John Duchi, Elad Hazan, and Yoram Singer, “Adaptive Subgradient Methods for Online Learning and Stochastic
Optimization,” Journal of Machine Learning Research, vol. 12, pp. 2121–2159, 2011.
[36] MD Zeiler, “Visualizing and understanding convolutional networks,” Springer Verlag, vol. 8689, pp. 818-833,
2014.
[37] Y Wu, “Google's Neural Machine Translation System: Bridging the Gap between Human and Machine
Translation,” 2016, [Online], Available: http://arxiv.org/abs/1609.08144, CoRR,
[38] Dean, J., Corrado, G. S., Monga, R., Chen, K., Devin, M., Le, Q. V, Ng, A. Y., “Large Scale Distributed Deep
Networks,” Neural Information Processing Systems, 2012.
[39] Pennington, J., Socher, R., and Manning, C. D., “Glove: Global Vectors for Word Representation,” Proceedings of
the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543, 2014.
BIOGRAPHIES OF AUTHORS
Zulaikha Kadim received her B.Eng degree in electronics engineering majoring in
telecommunication from Multimedia University, Malaysia in 2000 and currently persuing her Ph.D
degree in Universiti Kebangsaan Malaysia. She is also a researcher in national R&D institution.
Her current research interests are in video analytics, behavioral analysis and visual object tracking
Mohd Asyraf Zulkifley (M’18) received the B.Eng. degree in mechatronics from International
Islamic University Malaysia in 2008 and the Ph.D. degree in electrical and electronic engineering
from The University of Melbourne in 2012. He was a sponsored Researcher at the Department of
Computer Science, University of Oxford from 2016 to 2018. He is currently an Associate
Professor at the Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia.
His current research interests are visual object tracking and medical image analysis.
Nabilah Hamzah received her B.Scs (Electrical Eng.) degree from Universiti Teknologi Mara
(UiTM) in 2019 and currently persuing her ph.D degree at Universiti Teknologi Mara (UiTM).
Currently work as reseach assistant (RA) at UiTM under Trans-disciplinary Research Grant
Scheme (TRGS). Her current research interests are in object tracking, image translation and facial
recognition.