The exploration of oceans and sea beds is being made increasingly possible through the development of
Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it
must confront the existence of notable challenges. However, an automatic detecting and tracking system is
the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of
Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was
extracted by threshold segment and morphology process, and the features of invariant moment and area
were analysed. Results show that the method presented has the advantages of good robustness, high
accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images
and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained
underwater environment.
Two-Dimensional Object Detection Using Accumulated Cell Average Constant Fals...ijcisjournal
The extensive work in SONAR is oceanic Engineering which is one of the most developing researches in
engineering. The SideScan Sonars (SSS) are one of the most utilized devices to obtain acoustic images of
the seafloor. This paper proposes an approach for developing an efficient system for automatic object
detection utilizing the technique of accumulated cell average-constant false alarm rate in 2D (ACA-CFAR-
2D), where the optimization of the computational effort is achieved. This approach employs image
segmentation as preprocessing step for object detection, which have provided similar results with other
approaches like undecimated discrete wavelet transform (UDWT), watershed and active contour
techniques. The SSS sea bottom images are segmented for the 2D object detection using these four
techniques and the segmented images are compared along with the experimental results of the proportion
of segmented image (P) and runtime in seconds (T) are presented.
AUTOMATIC IDENTIFICATION OF CLOUD COVER REGIONS USING SURF ijcseit
Weather forecasting has become an indispensable application to predict the state of the atmosphere for a
future time based on cloud cover identification. But it generally needs the experience of a well-trained
meteorologist. In this paper, a novel method is proposed for automatic cloud cover estimation, typical to
Indian Territory Speeded Up Robust Feature Transform(SURF) is applied on the satellite images to obtain
the affine corrected images. The extracted cloud regions from the affine corrected images based on Otsu
threshold are superimposed on the artistic grids representing latitude and longitude over India. The
segmented cloud and grid composition drive a look up table mechanism to identify the cloud cover regions.
Owing to its simplicity, the proposed method processes the test images faster and provides accurate
segmentation for cloud cover regions.
ABSTRACT Feature extraction plays a vital role in the analysis and interpretation of remotely sensed data. The two important components of Feature extraction are Image enhancement and information extraction. Image enhancement techniques help in improving the visibility of any portion or feature of the image. Information extraction techniques help in obtaining the statistical information about any particular feature or portion of the image. This presented work focuses on the various feature extraction techniques and area of optical character recognition is a particularly important in Image processing. Keywords— Image character recognition, Methods for Feature Extraction, Basic Gabor Filter, IDA, and PCA.
Development and Hardware Implementation of an Efficient Algorithm for Cloud D...sipij
Detecting clouds in satellite imagery is becoming more important with increasing data availability which
are generated by earth observing satellites. Hence, intellectual processing of the enormous amount of data
received by hundreds of earth receiving stations, with specific satellite image oriented approaches,
presents itself as a pressing need. One of the most important steps in previous stages of satellite image
processing is cloud detection. While there are many approaches that compact with different semantic
meaning, there are rarely approaches that compact specifically with cloud and cloud cover detection. In
this paper, the technique presented is the scene based adaptive cloud, cloud cover detection and find the
position with assumption of sun reflection, background varying and scattering are constant. The capability
of the developed system was tested using dedicated satellite images and assessed in terms of cloud
percentage coverage. The system used for this process comprises of Intel(R) Xenon(R) CPU E31245 @
3.30GHz processor along with MATLAB 13 software and DSPC6713 processor along with Code Compose
Studio 3.1.
This paper represents a survey of various methods of video surveillance system which improves the security. The aim of this paper is to review of various moving object detection technics. This paper focuses on detection of moving objects in video surveillance system. Moving body detection is first important task for any video surveillance system. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey,paper described about optical flow method, Background subtraction, frame differencing to detect moving object. It also described tracking method based on Morphology technique.
Keywords -- Frame separation, Pre-processing, Object detection using frame difference, Optical flow,
Temporal Differencing and background subtraction. Object tracking
Two-Dimensional Object Detection Using Accumulated Cell Average Constant Fals...ijcisjournal
The extensive work in SONAR is oceanic Engineering which is one of the most developing researches in
engineering. The SideScan Sonars (SSS) are one of the most utilized devices to obtain acoustic images of
the seafloor. This paper proposes an approach for developing an efficient system for automatic object
detection utilizing the technique of accumulated cell average-constant false alarm rate in 2D (ACA-CFAR-
2D), where the optimization of the computational effort is achieved. This approach employs image
segmentation as preprocessing step for object detection, which have provided similar results with other
approaches like undecimated discrete wavelet transform (UDWT), watershed and active contour
techniques. The SSS sea bottom images are segmented for the 2D object detection using these four
techniques and the segmented images are compared along with the experimental results of the proportion
of segmented image (P) and runtime in seconds (T) are presented.
AUTOMATIC IDENTIFICATION OF CLOUD COVER REGIONS USING SURF ijcseit
Weather forecasting has become an indispensable application to predict the state of the atmosphere for a
future time based on cloud cover identification. But it generally needs the experience of a well-trained
meteorologist. In this paper, a novel method is proposed for automatic cloud cover estimation, typical to
Indian Territory Speeded Up Robust Feature Transform(SURF) is applied on the satellite images to obtain
the affine corrected images. The extracted cloud regions from the affine corrected images based on Otsu
threshold are superimposed on the artistic grids representing latitude and longitude over India. The
segmented cloud and grid composition drive a look up table mechanism to identify the cloud cover regions.
Owing to its simplicity, the proposed method processes the test images faster and provides accurate
segmentation for cloud cover regions.
ABSTRACT Feature extraction plays a vital role in the analysis and interpretation of remotely sensed data. The two important components of Feature extraction are Image enhancement and information extraction. Image enhancement techniques help in improving the visibility of any portion or feature of the image. Information extraction techniques help in obtaining the statistical information about any particular feature or portion of the image. This presented work focuses on the various feature extraction techniques and area of optical character recognition is a particularly important in Image processing. Keywords— Image character recognition, Methods for Feature Extraction, Basic Gabor Filter, IDA, and PCA.
Development and Hardware Implementation of an Efficient Algorithm for Cloud D...sipij
Detecting clouds in satellite imagery is becoming more important with increasing data availability which
are generated by earth observing satellites. Hence, intellectual processing of the enormous amount of data
received by hundreds of earth receiving stations, with specific satellite image oriented approaches,
presents itself as a pressing need. One of the most important steps in previous stages of satellite image
processing is cloud detection. While there are many approaches that compact with different semantic
meaning, there are rarely approaches that compact specifically with cloud and cloud cover detection. In
this paper, the technique presented is the scene based adaptive cloud, cloud cover detection and find the
position with assumption of sun reflection, background varying and scattering are constant. The capability
of the developed system was tested using dedicated satellite images and assessed in terms of cloud
percentage coverage. The system used for this process comprises of Intel(R) Xenon(R) CPU E31245 @
3.30GHz processor along with MATLAB 13 software and DSPC6713 processor along with Code Compose
Studio 3.1.
This paper represents a survey of various methods of video surveillance system which improves the security. The aim of this paper is to review of various moving object detection technics. This paper focuses on detection of moving objects in video surveillance system. Moving body detection is first important task for any video surveillance system. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey,paper described about optical flow method, Background subtraction, frame differencing to detect moving object. It also described tracking method based on Morphology technique.
Keywords -- Frame separation, Pre-processing, Object detection using frame difference, Optical flow,
Temporal Differencing and background subtraction. Object tracking
Three dimensional particle image velocimetrypawankumar9275
Three-dimensional particle image velocimetry for the flows near the wall. A brief introduction to measurement methods in fluid fields in general and imaging methods in particular. A general overview of famous three-dimensional particle imaging methods and detailed description of image velocimetry for the near wall flows
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.
Energy efficient sensor selection in visual sensor networks based on multi ob...ijcsa
In this paper, we investigate the problem of visual coverage in visual sensor networks (VSNs). It is required to select a subset of sensor nodes to provide a visual coverage over the monitoring region at each point of time. In contrast with the pervious works which considered only single metric for sensor selection method, in this study we assumed the sensor selection as multi-criteria problem. For the purpose of maximizing the network lifetime, we consider three metrics a) visual coverage ratio, i.e., percentage of monitoring region which is fully covered by camera sensors, b) number of selected sensors, i.e., number of active sensors for covering the desired region, and c) overlapping coverage ratio, i.e., percentage of monitoring region which is covered by more than one camera sensor. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the problem. Besides, impact of steady state selection and generational selection method is studied on the network lifetime. Simulation results show the superiority of multi-objective optimization. NSGA-II results not only longer network lifetime but also fewer number of active sensor and lower overlapping ratio at each point of time.
Under the certain circumstances of the low and unacceptable accuracy on image recognition, the feature
extraction method for optical images based on the wavelet space feature spectrum entropy is recently
studied. With this method, the principle that the energy is constant before and after the wavelet
transformation is employed to construct the wavelet energy pattern matrices, and the feature spectrum
entropy of singular value is extracted as the image features by singular value decomposition of the matrix.
At the same time, BP neural network is also applied in image recognition. The experimental results show
that high image recognition accuracy can be acquired by using the feature extraction method for optical
images proposed in this paper, which proves the validity of the method.
RAIN STREAKS ELIMINATION USING IMAGE PROCESSING ALGORITHMSsipij
The paper addresses the problem of rain streak removal from videos. While, Rain streak removal from scene is important but a lot of research in this area, robust and real time algorithms is unavailable in the market. Difficulties in the rain streak removal algorithm arises due to less visibility, less illumination, and availability of moving camera and objects. The challenge that plagues rain streak recovery algorithm is detecting rain streaks and replacing them with original values to recover the scene. In this paper, we discuss the use of photometric and chromatic properties for rain detection. Updated Gaussian Mixture Model (Updated GMM) has detected moving objects. This rain streak removal algorithm is used to detect rain streaks from videos and replace it with estimated values, which is equivalent to original value. The spatial and temporal properties are used to replace rain streaks with its original values.
The determination of Region-of-Interest has been recognised as an important means by which
unimportant image content can be identified and excluded during image compression or image
modelling, however existing Region-of-Interest detection methods are computationally
expensive thus are mostly unsuitable for managing large number of images and the compression
of images especially for real-time video applications. This paper therefore proposes an
unsupervised algorithm that takes advantage of the high computation speed being offered by
Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to
achieve fast and efficient Region-of-Interest detection.
CHARACTERIZING HUMAN BEHAVIOURS USING STATISTICAL MOTION DESCRIPTORsipij
Identifying human behaviors is a challenging research problem due to the complexity and variation of
appearances and postures, the variation of camera settings, and view angles. In this paper, we try to
address the problem of human behavior identification by introducing a novel motion descriptor based on
statistical features. The method first divide the video into N number of temporal segments. Then for each
segment, we compute dense optical flow, which provides instantaneous velocity information for all the
pixels. We then compute Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32
bins. We then compute statistical features from the obtained HOOF forming a descriptor vector of 192- dimensions. We then train a non-linear multi-class SVM that classify dif erent human behaviors with the
accuracy of 72.1%. We evaluate our method by using publicly available human action data set. Experimental results shows that our proposed method out performs state of the art methods.
Obstacle detection for autonomous systems using stereoscopic images and bacte...IJECEIAES
This paper presents a low cost strategy for real-time estimation of the position of ob- stacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.
Three dimensional particle image velocimetrypawankumar9275
Three-dimensional particle image velocimetry for the flows near the wall. A brief introduction to measurement methods in fluid fields in general and imaging methods in particular. A general overview of famous three-dimensional particle imaging methods and detailed description of image velocimetry for the near wall flows
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.
Energy efficient sensor selection in visual sensor networks based on multi ob...ijcsa
In this paper, we investigate the problem of visual coverage in visual sensor networks (VSNs). It is required to select a subset of sensor nodes to provide a visual coverage over the monitoring region at each point of time. In contrast with the pervious works which considered only single metric for sensor selection method, in this study we assumed the sensor selection as multi-criteria problem. For the purpose of maximizing the network lifetime, we consider three metrics a) visual coverage ratio, i.e., percentage of monitoring region which is fully covered by camera sensors, b) number of selected sensors, i.e., number of active sensors for covering the desired region, and c) overlapping coverage ratio, i.e., percentage of monitoring region which is covered by more than one camera sensor. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the problem. Besides, impact of steady state selection and generational selection method is studied on the network lifetime. Simulation results show the superiority of multi-objective optimization. NSGA-II results not only longer network lifetime but also fewer number of active sensor and lower overlapping ratio at each point of time.
Under the certain circumstances of the low and unacceptable accuracy on image recognition, the feature
extraction method for optical images based on the wavelet space feature spectrum entropy is recently
studied. With this method, the principle that the energy is constant before and after the wavelet
transformation is employed to construct the wavelet energy pattern matrices, and the feature spectrum
entropy of singular value is extracted as the image features by singular value decomposition of the matrix.
At the same time, BP neural network is also applied in image recognition. The experimental results show
that high image recognition accuracy can be acquired by using the feature extraction method for optical
images proposed in this paper, which proves the validity of the method.
RAIN STREAKS ELIMINATION USING IMAGE PROCESSING ALGORITHMSsipij
The paper addresses the problem of rain streak removal from videos. While, Rain streak removal from scene is important but a lot of research in this area, robust and real time algorithms is unavailable in the market. Difficulties in the rain streak removal algorithm arises due to less visibility, less illumination, and availability of moving camera and objects. The challenge that plagues rain streak recovery algorithm is detecting rain streaks and replacing them with original values to recover the scene. In this paper, we discuss the use of photometric and chromatic properties for rain detection. Updated Gaussian Mixture Model (Updated GMM) has detected moving objects. This rain streak removal algorithm is used to detect rain streaks from videos and replace it with estimated values, which is equivalent to original value. The spatial and temporal properties are used to replace rain streaks with its original values.
The determination of Region-of-Interest has been recognised as an important means by which
unimportant image content can be identified and excluded during image compression or image
modelling, however existing Region-of-Interest detection methods are computationally
expensive thus are mostly unsuitable for managing large number of images and the compression
of images especially for real-time video applications. This paper therefore proposes an
unsupervised algorithm that takes advantage of the high computation speed being offered by
Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to
achieve fast and efficient Region-of-Interest detection.
CHARACTERIZING HUMAN BEHAVIOURS USING STATISTICAL MOTION DESCRIPTORsipij
Identifying human behaviors is a challenging research problem due to the complexity and variation of
appearances and postures, the variation of camera settings, and view angles. In this paper, we try to
address the problem of human behavior identification by introducing a novel motion descriptor based on
statistical features. The method first divide the video into N number of temporal segments. Then for each
segment, we compute dense optical flow, which provides instantaneous velocity information for all the
pixels. We then compute Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32
bins. We then compute statistical features from the obtained HOOF forming a descriptor vector of 192- dimensions. We then train a non-linear multi-class SVM that classify dif erent human behaviors with the
accuracy of 72.1%. We evaluate our method by using publicly available human action data set. Experimental results shows that our proposed method out performs state of the art methods.
Obstacle detection for autonomous systems using stereoscopic images and bacte...IJECEIAES
This paper presents a low cost strategy for real-time estimation of the position of ob- stacles in an unknown environment for autonomous robots. The strategy was intended for use in autonomous service robots, which navigate in unknown and dynamic indoor environments. In addition to human interaction, these environments are characterized by a design created for the human being, which is why our developments seek morphological and functional similarity equivalent to the human model. We use a pair of cameras on our robot to achieve a stereoscopic vision of the environment, and we analyze this information to determine the distance to obstacles using an algorithm that mimics bacterial behavior. The algorithm was evaluated on our robotic platform demonstrating high performance in the location of obstacles and real-time operation.
Investigations on real time RSSI based outdoor target tracking using kalman f...IJECEIAES
Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn‟t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the nonanchor node is estimated after finding the distance between beacon and nonanchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.
A Novel Approach for Ship Recognition using Shape and Texture ijait
Maritime security includes reliable identification of ship entering and leaving a nation’s territorial waters. Sea target detection from remote sensing imagery is very important, with a wide array of applications in areas such as fishery management, vessel traffic services, and naval warfare. Automated systems that could identify a ship could complement existing electronic signal identification methods. A new classification approach using shape and texture is introduced for Ship detection. Texture information can improve classification performance. This approach uses both shape and texture features. Feature extraction is done by Hu’s moment invariants with several classification algorithms. This paper presents an overview of
automatic ship recognition methods with a view towards embedded implementation on optical smart cameras. Therefore this approach may attain a good classification rate.
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
Vehicle Tracking Using Kalman Filter and Featuressipij
Vehicle tracking has a wide variety of applications. The image resolution of the video available from most traffic camera system is low. In many cases for tracking multi object, distinguishing them from another isn’t easy because of their similarity. In this paper we describe a method, for tracking multiple objects, where the objects are vehicles. The number of vehicles is unknown and varies. We detect all moving objects, and for tracking of vehicle we use the kalman filter and color feature and distance of it from one frame to the next. So the method can distinguish and tracking all vehicles individually. The proposed algorithm can be applied to multiple moving objects.
A ROS IMPLEMENTATION OF THE MONO-SLAM ALGORITHMcsandit
Computer vision approaches are increasingly used in mobile robotic systems, since they allow
to obtain a very good representation of the environment by using low-power and cheap sensors.
In particular it has been shown that they can compete with standard solutions based on laser
range scanners when dealing with the problem of simultaneous localization and mapping
(SLAM), where the robot has to explore an unknown environment while building a map of it and
localizing in the same map. We present a package for simultaneous localization and mapping in
ROS (Robot Operating System) using a monocular camera sensor only. Experimental results in
real scenarios as well as on standard datasets show that the algorithm is able to track the
trajectory of the robot and build a consistent map of small environments, while running in near
real-time on a standard PC.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Speed Determination of Moving Vehicles using Lucas- Kanade AlgorithmEditor IJCATR
This paper presents a novel velocity estimation method for ground vehicles. The task here is to automatically estimate
vehicle speed from video sequences acquired with a fixed mounted camera. The vehicle motion is detected and tracked along the
frames using Lucas-Kanade algorithm. The distance traveled by the vehicle is calculated using the movement of the centroid over the
frames and the speed of the vehicle is estimated. The average speed of cars is determined from various frames. The application is
developed using MATLAB and SIMULINK.
Sensor Network for Landslide Monitoring With Laser Ranging System Avoiding Ra...Waqas Tariq
Sensor network for landslide monitoring with laser ranging system is developed together with landslide disaster relief with remote sensing satellite imagery data. Time diversity is utilized for rainfall influence avoidance in the distance measurements between laser ranging equipment and targets. Also automatic tie point extraction method is proposed. Experimental results show that (1) the proposed time diversity of the laser ranging measurement does work for avoidance from rainfall influence; (2) the proposed automatic control point extraction method does work for tie point matching together with change detection for landslide disaster relief.
ANALYSIS OF EXISTING TRAILERS’ CONTAINER LOCK SYSTEMS IJCSEIT Journal
Trailers carry large containers to various destinations in the world. These are manually locked on to
trailers as they move through these long distances. Security mainly refers to the safety of a state,
organization, property, and individuals against criminal activity. The study was made to analyze the
existing trailer locks and the insecurity being experienced currently. The study also focused on creating a
background to building an automated lock system for auto-mobiles. Findings showed that there are various
container types like the General Purpose containers, the Hard-Top containers and the Open-Top among
others. Similarly, the twist locks were the ones revised for this study. The study also discussed the
weaknesses of the twist locks, most especially the non-notification on unsecured locks. This causes leads to
accidents and wastage of lives and property. The study finally proposed an automated lock system to
overcome these weaknesses to some good extent.
A MODEL FOR REMOTE ACCESS AND PROTECTION OF SMARTPHONES USING SHORT MESSAGE S...IJCSEIT Journal
The smartphone usage among people is increasing rapidly. With the phenomenal growth of smartphone
use, smartphone theft is also increasing. This paper proposes a model to secure smartphones from theft as
well as provides options to access a smartphone through other smartphone or a normal mobile via Short
Message Service. This model provides option to track and secure the mobile by locking it. It also provides
facilities to receive the incoming call and sms information to the remotely connected device and enables the
remote user to control the mobile through SMS. The proposed model is validated by the prototype
implementation in Android platform. Various tests are conducted in the implementation and the results are
discussed.
BIOMETRIC APPLICATION OF INTELLIGENT AGENTS IN FAKE DOCUMENT DETECTION OF JOB...IJCSEIT Journal
The Job selection process in today’s globally competitive economy can be a daunting task for prospective
employees no matter their experience level. Although many years of research has been devoted to job
search and application resulting in good integration with information technology including the internet and
intelligent agent-based architectures, there are still many areas that need to be enhanced. Two such areas
include the quality of jobs associated with applicants in the job search by profiling the needs of employers
against the needs of prospective employees and the security and verifications schemes integrated to reduce
the instances of fraud and identity theft. The integration of mobile, intelligent agent, and cryptography
technologies provide benefits such as improved accessibility wirelessly, intelligent dynamic profiling, and
increased security. With this in mind we propose the intelligent mobile agents instead of human agents to
perform the Job search using fuzzy preferences which is been published elsewhere and application
operations incorporating the use of agents with a trust authority to establish employer trust and validate
applicant identity and accuracy. Our proposed system incorporates design methodologies to use JADELEAP
and Android to provide a robust, secure, user friendly solution.
FACE RECOGNITION USING DIFFERENT LOCAL FEATURES WITH DIFFERENT DISTANCE TECHN...IJCSEIT Journal
A face recognition system using different local features with different distance measures is proposed in this
paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values,
Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local
features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector
and diagonal vectors are computed for these matrices. Global feature vector is generated for face
recognition. Experiments are performed on benchmark face YALE database. Results indicate that the
proposed method gives better recognition performance in terms of average recognized rate and retrieval
time compared to the existing methods.
BIOMETRICS AUTHENTICATION TECHNIQUE FOR INTRUSION DETECTION SYSTEMS USING FIN...IJCSEIT Journal
Identifying attackers is a major apprehension to both organizations and governments. Recently, the most
used applications for prevention or detection of attacks are intrusion detection systems. Biometrics
technology is simply the measurement and use of the unique characteristics of living humans to distinguish
them from one another and it is more useful as compare to passwords and tokens as they can be lost or
stolen so we have choose the technique biometric authentication. The biometric authentication provides the
ability to require more instances of authentication in such a quick and easy manner that users are not
bothered by the additional requirements. In this paper, we have given a brief introduction about
biometrics. Then we have given the information regarding the intrusion detection system and finally we
have proposed a method which is based on fingerprint recognition which would allow us to detect more
efficiently any abuse of the computer system that is running.
PERFORMANCE ANALYSIS OF FINGERPRINTING EXTRACTION ALGORITHM IN VIDEO COPY DET...IJCSEIT Journal
A video fingerprint is a recognizer that is derived from a piece of video content. The video fingerprinting
methods obtain unique features of a video that differentiates one video clip from another. It aims to identify
whether a query video segment is a copy of video from the video database or not based on the signature of
the video. It is difficult to find whether a video is a copied video or a similar video, since the features of the
content are very similar from one video to the other. The main focus of this paper is to detect that the query
video is present in the video database with robustness depending on the content of video and also by fast
search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithms are adopted in
this paper to achieve robust, fast, efficient and accurate video copy detection. As a first step, the
Fingerprint Extraction algorithm is employed which extracts a fingerprint through the features from the
image content of video. The images are represented as Temporally Informative Representative Images
(TIRI). Then, the second step is to find the presence of copy of a query video in a video database, in which
a close match of its fingerprint in the corresponding fingerprint database is searched using inverted-filebased
method. The proposed system is tested against various attacks like noise, brightness, contrast,
rotation and frame drop. Thus the performance of the proposed system on an average shows high true
positive rate of 98% and low false positive rate of 1.3% for different attacks.
Effect of Interleaved FEC Code on Wavelet Based MC-CDMA System with Alamouti ...IJCSEIT Journal
In this paper, the impact of Forward Error Correction (FEC) code namely Trellis code with interleaver on
the performance of wavelet based MC-CDMA wireless communication system with the implementation of
Alamouti antenna diversity scheme has been investigated in terms of Bit Error Rate (BER) as a function of
Signal-to-Noise Ratio (SNR) per bit. Simulation of the system under proposed study has been done in M-ary
modulation schemes (MPSK, MQAM and DPSK) over AWGN and Rayleigh fading channel incorporating
Walsh Hadamard code as orthogonal spreading code to discriminate the message signal for individual
user. It is observed via computer simulation that the performance of the interleaved coded based proposed
system outperforms than that of the uncoded system in all modulation schemes over Rayleigh fading
channel.
FUZZY WEIGHTED ASSOCIATIVE CLASSIFIER: A PREDICTIVE TECHNIQUE FOR HEALTH CARE...IJCSEIT Journal
In this paper we extend the problem of classification using Fuzzy Association Rule Mining and propose the
concept of Fuzzy Weighted Associative Classifier (FWAC). Classification based on Association rules is
considered to be effective and advantageous in many cases. Associative classifiers are especially fit to
applications where the model may assist the domain experts in their decisions. Weighted Associative
Classifiers that takes advantage of weighted Association Rule Mining is already being proposed. However,
there is a so-called "sharp boundary" problem in association rules mining with quantitative attribute
domains. This paper proposes a new Fuzzy Weighted Associative Classifier (FWAC) that generates
classification rules using Fuzzy Weighted Support and Confidence framework. The naïve approach can be
used to generating strong rules instead of weak irrelevant rules. where fuzzy logic is used in partitioning
the domains. The problem of Invalidation of Downward Closure property is solved and the concept of
Fuzzy Weighted Support and Fuzzy Weighted Confidence frame work for Boolean and quantitative item
with weighted setting is generalized. We propose a theoretical model to introduce new associative classifier
that takes advantage of Fuzzy Weighted Association rule mining.
GENDER RECOGNITION SYSTEM USING SPEECH SIGNALIJCSEIT Journal
In this paper, a system, developed for speech encoding, analysis, synthesis and gender identification is
presented. A typical gender recognition system can be divided into front-end system and back-end system.
The task of the front-end system is to extract the gender related information from a speech signal and
represents it by a set of vectors called feature. Features like power spectrum density, frequency at
maximum power carry speaker information. The feature is extracted using First Fourier Transform (FFT)
algorithm. The task of the back-end system (also called classifier) is to create a gender model to recognize
the gender from his/her speech signal in recognition phase. This paper also presents the digital processing
of a speech signals (pronounced “A” and “B”) which are taken from 10 persons, 5 of them are Male and
the rest of them are Female. Power Spectrum Estimation of the signal is examined .The frequency at
maximum power of the English Phonemes is extracted from the estimated power spectrum. The system uses
threshold technique as identification tool. The recognition accuracy of this system is 80% on average.
DETECTION OF CONCEALED WEAPONS IN X-RAY IMAGES USING FUZZY K-NNIJCSEIT Journal
Scanning baggage by x-ray and analysing such images have become important technique for detecting
illicit materials in the baggage at Airports. In order to provide adequate security, a reliable and fast
screening technique is needed for baggage examination.This paper aims at providing an automatic method
for detecting concealed weapons, typically a gun in the baggage by employing image segmentation method
to extract the objects of interest from the image followed by applying feature extraction methods namely
Shape context descriptor and Zernike moments. Finally the objects are classified using fuzzy KNN as illicit
or non-illicit object.
META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVALIJCSEIT Journal
The proposed approach avoids the semantic gap in image retrieval by combining automatic relevance
feedback and a modified stochastic algorithm. A visual feature database is constructed from the image
database, using combined feature vector. Very few fast-computable features are included in this step. The
user selects the query image, and based on that, the system ranks the whole dataset. The nearest images are
retrieved and the first automatic relevance feedback is generated. The combined similarity of textual and
visual feature space using Latent Semantic Indexing is evaluated and the images are labelled as relevant or
irrelevant. The feedback drives a feature re-weighting process and is routed to the particle swarm
optimizer. Instead of classical swarm update approach, the swarm is split, for each swarm to perform the
search in parallel, thereby increasing the performance of the system. It provides a powerful optimization
tool and an effective space exploration mechanism. The proposed approach aims to achieve the following
goals without any human interaction - to cluster relevant images using meta-heuristics and to dynamically
modify the feature space by feeding automatic relevance feedback.
ERROR PERFORMANCE ANALYSIS USING COOPERATIVE CONTENTION-BASED ROUTING IN WIRE...IJCSEIT Journal
In Wireless Ad hoc network, cooperation of nodes can be achieved by more interactions at higher protocol
layers, particularly the MAC (Medium Access Control) and network layers play vital role. MAC facilitates
a routing protocol based on position location of nodes at network layer specially known as Beacon-less
geographic routing (BLGR) using Contention-based selection process. This paper proposes two levels of
cross-layer framework -a MAC network cross-layer design for forwarder selection (or routing) and a
MAC-PHY for relay selection. Wireless networks suffers huge number of communication at the same time
leads to increase in collision and energy consumption; hence focused on new Contention access method
that uses a dynamical change of channel access probability which can reduce the number of contention
times and collisions. Simulation result demonstrates the best Relay selection and the comparative of direct
mode with the cooperative networks. And also demonstrates the Performance evaluation of contention
probability with Collision avoidance.
M-FISH KARYOTYPING - A NEW APPROACH BASED ON WATERSHED TRANSFORMIJCSEIT Journal
Karyotyping is a process in which chromosomes in a dividing cell are properly stained, identified and
displayed in a standard format, which helps geneticist to study and diagnose genetic factors behind various
genetic diseases and for studying cancer. M-FISH (Multiplex Fluorescent In-Situ Hybridization) provides
color karyotyping. In this paper, an automated method for M-FISH chromosome segmentation based on
watershed transform followed by naive Bayes classification of each region using the features, mean and
standard deviation, is presented. Also, a post processing step is added to re-classify the small chromosome
segments to the neighboring larger segment for reducing the chances of misclassification. The approach
provided improved accuracy when compared to the pixel-by-pixel approach. The approach was tested on
40 images from the dataset and achieved an accuracy of 84.21 %.
Steganography is the technique of hiding a confidential message in an ordinary message and the extraction
of that secret message at its destination. Different carrier file formats can be used in steganography.
Among these carrier file formats, digital images are the most popular. For this work, digital images are
used. Here steganography is done on the skin portion of an image. First skin portion of an image is
detected. Random pixels are selected from that detected region using a pseudo-random number generator.
The bits of the secret message will be embedded on the LSB of these random pixels. An analysis is done to
check the efficiency and robustness of the proposed method. The aim of this work is to show that
steganography done using random pixel selection is less prone to outside attacks.
A NOVEL WINDOW FUNCTION YIELDING SUPPRESSED MAINLOBE WIDTH AND MINIMUM SIDELO...IJCSEIT Journal
In many applications like FIR filters, FFT, signal processing and measurements, we are required (~45 dB)
or less side lobes amplitudes. However, the problem is usual window based FIR filter design lies in its side
lobes amplitudes that are higher than the requirement of application. We propose a window function,
which has better performance like narrower main lobe width, minimum side lobe peak compared to the
several commonly used windows. The proposed window has slightly larger main lobe width of the
commonly used Hamming window, while featuring 6.2~22.62 dB smaller side lobe peak. The proposed
window maintains its maximum side lobe peak about -58.4~-52.6 dB compared to -35.8~-38.8 dB of
Hamming window for M=10~14, while offering roughly equal main lobe width. Our simulated results also
show significant performance upgrading of the proposed window compared to the Kaiser, Gaussian, and
Lanczos windows. The proposed window also shows better performance than Dolph-Chebyshev window.
Finally, the example of designed low pass FIR filter confirms the efficiency of the proposed window.
CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...IJCSEIT Journal
Association rule mining (ARM) is the process of generating rules based on the correlation between the set
of items that the customers purchase.Of late, data mining researchers have improved upon the quality of
association rule mining for business development by incorporating factors like value (utility), quantity of
items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead
to a probable loss of profitable rules.
The advantage of wealth of the customers’ needs information and rules aids the retailer in designing his
store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified
version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the
customers based on some criteria; for example, a retail business may need to identify valuable customers
who are major contributors to a company’s overall profit. For a potential customer arriving in the store,
which customer group one should belong to according to customer needs, what are the preferred functional
features or products that the customer focuses on and what kind of offers will satisfy the customer, etc.,
finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
FACTORS AFFECTING ACCEPTANCE OF WEB-BASED TRAINING SYSTEM: USING EXTENDED UTA...IJCSEIT Journal
Advancement in information system leads organizations to apply e-learning system to train their employees
in order to enhance its performance. In this respect, applying web based training will enable the
organization to train their employees quickly, efficiently and effectively anywhere at any time. This
research aims to extend Unified Theory of Acceptance and Use Technology (UTAUT) using some factors
such flexibility of web based training system, system interactivity and system enjoyment, in order to explain
the employees’ intention to use web based training system. A total of 290 employees have participated in
this study. The findings of the study revealed that performance expectancy, facilitating conditions, social
influence and system flexibility have direct effect on the employees’ intention to use web based training
system, while effort expectancy, system enjoyment and system interactivity have indirect effect on
employees’ intention to use the system.
PROBABILISTIC INTERPRETATION OF COMPLEX FUZZY SETIJCSEIT Journal
The innovative concept of Complex Fuzzy Set is introduced. The objective of the this paper to investigate
the concept of Complex Fuzzy set in constraint to a traditional Fuzzy set , where the membership function
ranges from [0, 1], but in the Complex fuzzy set extended to a unit circle in a complex plane, where the
member ship function in the form of complex number. The Compressive study of mathematical operation of
Complex Fuzzy set is presented. The basic operation like addition, subtraction, multiplication and division
are described here. The Novel idea of this paper to measure the similarity between two fuzzy relations by
evaluating δ -equality. Here also we introduce the probabilistic interpretation of the complex fuzzy set
where we attempted to clarify the distinction between Fuzzy logic and probability.
ALGORITHMIC AND ARCHITECTURAL OPTIMIZATION OF A 3D RECONSTRUCTION MEDICAL IMA...IJCSEIT Journal
This paper presents an optimization of an FPGA circuit implementation of 3D reconstruction algorithm of
medicals images. It is based on an algorithmic specification in the shape of a Factorized and Conditioned
Data Dependences Graph (GFCDD). An automatic and optimized implementation of the algorithm of «
Marching Cubes » has been carried out. The repetitive property of the algorithm has been exploited, as
much as possible, by means of the methodology “Adequacy Algorithm Structures”.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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.
AN EFFICIENT IMPLEMENTATION OF TRACKING USING KALMAN FILTER FOR UNDERWATER ROBOT APPLICATION
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
DOI : 10.5121/ijcseit.2012.2207 67
AN EFFICIENT IMPLEMENTATION OF TRACKING
USING KALMAN FILTER FOR UNDERWATER
ROBOT APPLICATION
Nagamani Modalavalasa1
, G SasiBhushana Rao2
, K. Satya Prasad3
1
Dept.of ECE, SBTET, Andhra Pradesh, INDIA
mani.modalavalasa@gmail.com
2
Dept. of ECE, Andhra University, Visakhapatnam, Andhra Pradesh, INDIA
3
Dept.of ECE, Jawaharlal Nehru Technological University Kakinada, Kakinada, INDIA
ABSTRACT
The exploration of oceans and sea beds is being made increasingly possible through the development of
Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it
must confront the existence of notable challenges. However, an automatic detecting and tracking system is
the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of
Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was
extracted by threshold segment and morphology process, and the features of invariant moment and area
were analysed. Results show that the method presented has the advantages of good robustness, high
accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images
and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained
underwater environment.
KEYWORDS
Autonomous Underwater Vehicle, Tracking, SONAR, Threshold, Obstacle avoidance
1. INTRODUCTION
Autonomous underwater vehicles (AUVs) have the potential to revolutionize our access to the
oceans to address critical problems facing the marine community such as underwater search and
mapping, climate change assessment, marine habitat monitoring, and shallow water mine
countermeasures. Navigation is one of the primary challenges in AUV research today.
Navigation is an important requirement for any type of mobile robot, but this is especially true for
autonomous underwater vehicles. Good navigation information is essential for safe operation and
recovery of an AUV. For the data gathered by an AUV to be of value, the location from which the
data has been acquired must be accurately known. Some of the important concerns for AUV
navigation, such as the effects of acoustic propagation are unique to the ocean environment.
The goal of this paper is the Surveillance using Imaging Sonar Data for Underwater Robot
Application based on Kalman filter. In this paper, the images from the Compressed High Intensity
Radar Pulse (CHIRP) Sonar are used for the analysis. CHIRP Sonars are the active Sonars
invented to overcome the limitations of conventional monotonic Sonars. In conventional Sonars
when the separation of the targets is less than the range resolution, then it displays as a single
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
68
large combined target rather than the multiple smaller targets. On the other hand, if we use the
smaller transmission pulse to increase the range resolution, then the maximum range obtained
decreases due to less energy. In order to overcome this problem, CHIRP Sonars have been
developed and made use of. . In this paper Sector Scan SONAR images are taken as input data
and processed for obtaining the tracking results.
Several methods are available for tracking the objects in the image sequences received from the
Sonar fitted on the AUV. These methods are not suitable for undertaking the collision avoidance
if the AUV is required to be controlled in the constrained underwater environment. The time
required for extraction of target parameters and for subsequent tracking becomes an important
criterion because the AUV is required to be maneuvered well before the collision occurs. A
comparison of the commonly used algorithms for data association and tracking namely Nearest
Neighbour Kalman filter (NNKF) and Probabilistic data association filter (PDAF) is made in ref.
[1] for single target tracking in clutter. In this paper, tracking of the objects (detected in the
sequence of images received from Sonar) based on their centroids has been presented.
Accordingly the calculation of the centroid, tracking of the objects and the calculation of the
trajectories has been presented.
2. IMAGE PROCESSING MODEL BASED ON CENTROID OF THE OBJECT
In image-based air traffic control or air defense system, automatic detection and tracking of
targets are extremely important for their safety or early warning. In such scenario, the sensor
images are often cluttered, dim, spurious or noisy due to the fact that the distances to targets from
the control centre are large. Tracking problems involve processing measurements from a target of
interest and producing at each time step, an estimate of the target’s current position and velocity
vectors. Uncertainties in the target motion and in the measured values, usually modelled as
additive random noise, lead to corresponding uncertainties in the target state. Also, there is
additional uncertainty regarding the origin of the received data, which may or may not include
measurements from the targets and may be due to random clutter (false alarms). This leads to the
problem of data association [2]. In this situation tracking algorithms have to include information
on detection and false alarm probabilities. This approach provides a method of centroid tracking
and target identity estimation using image SONAR data.
The Centroid tracking combines both object and motion recognition characteristics for practical
target tracking from imaging sensors. The characteristics of the image considered are the intensity
and size of the cluster. The pixel intensity is discretised into several layers of gray level intensities
and it is assumed that sufficient target pixel intensities are within the limits of certain target
layers. The centroid tracking implementation involves the conversion of the image into a binary
image and applying upper and lower threshold limits for the “target layers”. The binary target
image is then converted to clusters by using nearest neighbour criterion. If the target size is
known, then it is used to set limits for removing those clusters that differ sufficiently from the
size of the target cluster to reduce computational complexity. The centroid of the clusters is then
calculated and this information is used for tracking the target. The Centroid tracking involves the
following steps:
a. Pre-processing to remove the noise / blur from the images. (In present day
applications this step is generally performed by the Sonar)
b. Identifying potential targets by image segmentation methods. Real Sonar image
and the image after segmentation are shown in Figure 1 and Figure 2. In this,
the image is segmented into objects, shadow and sea bottom reverberation
regions and then the edges of the object are extracted [3].
c. Calculation of the centroids for all the detected objects.
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
69
d. The steps (a) to (c) are performed on all subsequent images
e. Identification of the moving and stationary objects.
f. Determination of the association of the moving objects based on the maximum
speed criterion.
g. Tracking of the moving objects using Kalman Filter [4, 5]
h. Calculation of the trajectory.
i. Calculation of Collision course by taking the own speed and direction into
consideration [6].
j. Finally executing the manoeuvring commands to AUV
3. TRACKING ALGORITHM
The sequence of images can either be processed in real-time, coming directly from a video
camera for example, or it can be performed on a recorded set of images. The implementation of
this paper uses recorded image sequences although the theory can be applied to both types of
applications. The target to be tracked might be a complete object or a small area on an object. In
either case, the feature of interest is typically contained within a target region. In this paper will
consider target centroid positions across the image plane. The position will be described in X-Y
coordinates in pixel units on the image ( i.e. image coordinates).
Figure 2. Object identification from
the proposed segmentation method
Figure 1. Real SONAR image
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
70
Once objects are detected, we extract their outline and track their centroids across the image plane
using separate linear Kalman filters to estimate their x and y coordinates. The Kalman filter
provides a general solution to the recursive minimised mean square linear estimation problem.
The mean square error will be minimised as long as the target dynamics and the measurement
noise are accurately modelled. In addition, the Kalman filter provides a convenient measure of the
estimation accuracy through the covariance matrix, and the gain sequence automatically adapts to
the variability of the data. Figure 3 explains the steps, those have been implemented to track the
objects in the images received from the imaging Sonar of AUV. A linear, discrete-time dynamic
system describing the target parameters estimation is shown in Figure 4.
4. KALMAN FILTER
Derivations of the Kalman filter are available in the literature, e.g. [7], [8] and [9]. The transition
from one state to the next could be described in many ways. These different alternatives can be
grouped into linear and non-linear functions describing the state transition. Although it is possible
to handle either of these transition types, the standard Kalman filter employs a linear transition
function. The extended Kalman filter (EKF) allows a non-linear
transition, together with a non-linearmeasurement relationship. For the standard Kalman filter, the
state transition from k-1 to k can be expressed with the equation
xk = Axk-1 + wk-1 (1)
where A is referred to as the state transition matrix and w k-1 is a noise term. This noise term is a
Gaussian random variable with zero mean and a covariance matrix Q, so its probability
distribution is
Calculation of object
centroid in the image
Comparison of centroid
values in all subsequent
frames
Identification of moving
objects based on change
in centroid values
Separation of moving
objects from the
stationary objects
Calculation of speed and
direction of moving
objects
Calculation of trajectory
for all moving objects
Fig.3 Block diagram of the complete model
Fig. 4 Target aspects estimation and tracking from Kalman filter
Z-1 xk Hk∑ ∑
Ak+1, k
vk
wk zk
Process equation Measurement equation
5. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
71
p(w) ~ N(0,Q) (2)
The covariance matrix Q will be referred to as the process noise covariance matrix in the
remainder of this report. It accounts for possible changes in the process between k-1 and k that
are not already accounted for in the state transition matrix. Another assumed property of wk-1 is
that it is independent of the state xk-1 .
It is also necessary to model the measurement process, or the relationship between the state and
the measurement. In a general sense, it is not always possible to observe the process directly (i.e.
all the state parameters are observable without error). Some of the parameters describing the state
may not be observable at all, measurements might be scaled parameters, or possibly a
combination of multiple parameters. Again, the assumption is made that the relationship is linear.
So the measurement zk can be expressed in terms of the state xk [10] with
zk = Hxk + vk (3)
where H is an m × n matrix which relates the state to the measurement. Much like w k-1 for the
process, vk-1 is the noise of the measurement. It is also assumed to have a normal distribution
expressed by
p(v) ~ N(0,R) (4)
where R is the covariance matrix referred to as measurement noise covariance matrix.
In our analysis, the state xk contains the position (x, y) of the object at the instant k and also the
speed of the object in both x ( x& ) and y ( y& ) directions [11]. The new position (xk, yk) is the old
position (xk-1, yk-1) plus the velocity ( 1−kx& , 1−ky& ) plus noise w k-1.
The state equation, Eq. (5) is then in this case is defined as
1
1
1
1
1
1000
0100
010
001
−
−
−
−
−
+
=
k
k
k
k
k
k
k
k
k
w
y
x
y
x
t
t
y
x
y
x
&
&
&
&
(5)
Where ‘t’ represents the time interval between any two successive image frames and is
considered as 1second. And the measurement equation Eq. (6) is written as
k
k
k
k
k
kmeas
kmeas
k v
y
x
y
x
y
x
z +
=
=
&
&0010
0001
(6)
Where xkmeas and ykmeas are the measured positions in x and y directions.
The Kalman filter estimates a process by using a form of feedback control: the filter estimates the
process state at some time and then obtains feedback in the form of (noisy) measurements. As
6. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
72
such, the equations for the Kalman filter fall into two groups: time update equations and
measurement update equations. The time update equations are responsible for projecting forward
(in time) the current state and error covariance estimates to obtain the priori estimates for the next
time step. The measurement update equations are responsible for the feedback
i.e. for incorporating a new measurement into the priori estimate to obtain an improved
posteriori estimate.
Figure 5. Kalman filter cycle
As shown in Figure 5, the time update projects the current state estimate ahead in time and the
measurement update adjusts the projected estimate by an actual measurement at the time.
The time update equations can also be thought of as predictor equations, while the measurement
update equations can be thought of as corrector equations.
The main steps of Kalman Filtering algorithm that has been implemented in this paper are as
follows:-
Time Update ( Predict ) equations:
Step 1: Project the state ahead:
kkk wxAx += −
−
1
ˆˆ (7)
Step 2: Project the error covariance ahead:
QAAPP T
kk += −
−
1 (8)
Measurement Update ( Correct ) equations:
Step 3: Compute the Kalman gain:
1
)( −−−
+= RHHPHPK T
k
T
kk (9)
Step 4: Update estimation with measurements:
)ˆ(ˆˆ −−
−+= kkkkk xHzKxx (10)
Step 5: Update the error covariance:
−
−= kkk PHKIP )(
(11)
Step 6: Go to Step 1.
Time Update
( “Predict” )
Measurement Update
( “Predict” )
7. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
73
Steps 1 and 2 are responsible for projecting forward (in time) the current state and error
covariance (
−
kP ) estimates to obtain the a priori (
−
kxˆ ) estimates for the next time step. Steps 3
to 5 are responsible for the feedback i.e. for incorporating a new measurement into the a priori
estimate to obtain an improved a posteriori estimate ( kxˆ ).The Kalman gain Kk i.e. equation (9)
in (step 3) is chosen to be the gain that minimizes the posteriori error covariance. The next step is
to actually measure the process to obtain zk , and then to generate a posteriori state estimate by
incorporating the measurement as in equation (10). The final step is to obtain a posteriori error
covariance estimate via equation (11).
After each time and measurement update pair, the process is repeated with the previous posteriori
estimates used to project or predict the new priori estimates. This recursive nature is one of the
very appealing features of the Kalman filter. It makes practical implementations much more
feasible than (for example) an implementation of a Wiener filter (Brown and Hwang 1996) which
is designed to operate on all of the data directly for each estimate. The Kalman filter instead
recursively conditions the current estimate on all of the past measurements.
5. DATA ASSOCIATION
In many applications, knowledge of which target in the current frame relates to which target in
the previous frame is important and so the data association problem would need to be addressed.
Traditional multi-target tracking is based on coupling trackers such as Kalman filters, extended
Kalman filters or particle filters with a data association technique (Bar-Shalom [12] provides a
comprehensive treatment). The aim of the data association process is to interpret which
measurements are due to the targets and which are due to false alarms. An example of this used
on forward-looking sonar data is shown in [13]. Another technique which has been applied to
sonar imagery uses Optical Flow calculations to estimate direction motion [14].
Data Association plays a very important role in all tracking applications, especially in the
environment which is heterogeneous and having frequent occlusion conditions. This is aptly
applicable to the undersea environment where AUV operates.
To check that the objects those have appeared in the subsequent images actually belong to the
same target or not, following two conditions have been considered in our model [6]:-
a. The maximum pixels by which the centroid values will change in subsequent
frames, if the objects are moving with the maximum speed.
b. The variation in the position of the centroid due to the movement of water body/
occlusion.
For calculating the maximum pixels by which the centroid values will change in subsequent
frames, following model has been proposed:-
a. Assume, maximum speed that any object can have is ‘X’ km/hr.
b. The time interval between every two subsequent frames is ‘n’ seconds.
c. Image size is assumed to be kxk (for example 600x600).
d. The Range of the Sonar (R) has been taken in terms of meters.
Based on the above inputs, the maximum amount of distance (in meters) that an object will cover
when it is moving with maximum speed (X) is calculated. The distance is calculated as follows:-
8. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
74
a. Maximum distance that will be covered by the objects between consecutive
frames is [n*(X*1000/3600) ] Meters
b. The resolution of the image is R/ k meters per pixels both in row and column.
Therefore the maximum distance that will be covered in terms of pixels is given
by
[(X*1000/3600)*n]*[R/k] (12)
In this paper, following values have been selected:-
Maximum speed of the object: X = 8 km/hr.
Time difference between two subsequent frames as 1sec.
The Size of the Sonar image as 600x600.
The Sonar range (R) as 10 meters.
The resolution of the image is therefore (R/k) 10/600=0.0167 meters/pixel for both row and
column pixels.For any specified range up to 300mts, sonar can scan 0o to 360o
. While taking the
sonar data the sector is limited to 120o
and the range is kept to 10mts. Since the image size is of
600x600 where each column containing 600 pixels exactly covers 10mts range. Hence in any
direction throughout the 120o
sector of the image, the resolution of the image is 0.0167 mts/pixel.
By substituting the above values in the Eq. (12), the maximum distance in terms of meters that
will be covered by the object for every 1 second is calculated as 8.889 meters. Therefore, if the
distance between the centroids of object in the consecutive images is less than or equal to the
8.889 meters then it is assumed to be from the same object i.e. they are said to be associated.
Inherently, there is a variation in the position of the centroid due to the movement of water body.
By comparing the centroid values of the associated object in subsequent frames, if the difference
between the centroids is non-zero and is also greater than the variation in the position of the
centroid due to the movement of water body, object is said to be associated and moving. Similarly
if the centroid remains within the specified values (depends on the water body movement) in all
subsequent frames then it is assumed to be stationary. The speed of the moving object is
calculated accordingly.
Once the data association has been applied, we can initiate the tracking. Three cases are then
possible:
a. There is a new observation matching the predicted position. The Kalman filter
recursion is applied, a new state vector derived and new internal values
computed.
b. No new observation matches the prediction. The obstacle prediction is updated
using the Kalman filter internal values which are not updated. If no match is
found between the observations and a given tracked object on a predefined
number of frames, the tracked object is discarded as a false alarm.
c. An observation is not associated with any tracked object, a new object is created
and its corresponding Kalman filter initialized.
By invoking the tracking algorithm for the moving objects, their trajectories are then calculated.
6. RESULTS AND DATA PROCESSING
To acquire the required data, several experiments have been conducted at the Towing Tank of the
DRDO (NSTL), Visakhapatnam. The experiments included different scenarios such as object is
moving and Sonar remains stationary; Sonar is moving and object remains stationary; and both
Sonar and object are moving. The Towing tank is 500 meters in length with 8 meters in depth. In
9. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
75
this paper, we have implemented the proposed algorithm for the specific case i.e. object moving
and Sonar remains stationary. In this experiment, Digital Sector Scan (DSS) Sonar is fixed to
carriage and kept stationary at one place in the water, and object is moved manually towards the
Sonar. During data collection, the sector of Sonar is set to 120o
. The recorded video was then
converted into image frames using the ‘SeanetImageOut.exe’ software supplied along with the
Sonar. For the real-time implementation of the proposed algorithm, the resolution is kept low so
as to get the images instaneously at high speed.
Table 1. Comparison of Measured and Estimated Object positions (CENTROIDS)
Measured positions Estimated positions
Frames x y
coordinates
x y
coordinates
Frame1 317.3450 107.5848 317.3561 107.5907
Frame2 368.2032 187.0239 368.1796 187.0015
Frame3 473.6369 251.8636 474.2643 252.2195
Frame4 475.9537 288.6233 475.6405 288.4390
Frame5 394.5096 336.3308 393.3391 335.6495
Frame6 359.0980 398.4558 358.3820 398.0353
Frame7 370.5565 463.4487 370.2715 463.2771
The images were generated at every 1 second interval. These images were segmented so as to
identify the object and this was followed by the estimation of trajectory using Kalman Filter. In
the proposed model the objects have been associated by using the Data Association algorithm as
discussed in section 2.2. From the results the Mean Square Error for x-coordinate is found to be
0.3509 meters and for the y-coordinate is found to be 0.1188 meters. The accuracy of the object
positions is found to be ±0.0486meters. Table 1 shows the measured and estimated positions (in
terms of pixel values) of the objects in 7 subsequent frames. The final result has been shown in
Figure 5 which is a polar plot shows tracking of the object using the measured as well as the
estimated positions. From the proposed model, it is observed that time taken to identify the
objects through segmentation and extraction of obstacle parameters such as their range, bearing,
size, shape, speed and course using Kalman filter is approximately 0.4 seconds which is
reasonably less and aptly applicable for the application of obstacle avoidance. For every frame,
the complete processing takes 0.4 seconds and the time interval between the input frames is 1
second.
The sonar used to collect the data is Super Seeking DST (Digital Sonar Technology) Dual
Frequency CHIRP Sonar. Sector scan sonar with the following specifications:
Operating frequency (low)-Chirping from 250 to 350 kHz (300)
Operating frequency (high)-Chirping from 620 to720 kHz (670)
Optional high frequency 1 MHz
Beamwidth, vertical 20° [300]
Beamwidth, vertical 40° [670]
Beamwidth, horizontal 3.0° [300]
10. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
76
Beamwidth, horizontal 1.5° [670]
Maximum range 300 m [300]
Maximum range 100 m [670]
Minimum range 0.4 m
Scanned sector Variable to 360°
Range resolution 5 - 400 mm depending on range
x
y
observed
estimated
Figure 6. Object Tracking Using Kalman
7. CONCLUSIONS
Considering the fact that the time taken for calculation of target parameters for the purpose of
obstacle avoidance must be as less as possible and the position should be as accurate as possible,
the algorithm that has been proposed and validated in this paper can be concluded to meet the
given requirement. It is seen from the results that the time taken for undertaking complete
processing on every image is approximately 0.4 Seconds and the positional accuracy is found to
be ±0.0486 meters. It implies that if the obstacle is detected at the range of 300 meters and if the
AUV is moving with the speed of 8 Km/Hr then the sufficient time is available with the AUV to
take corrective course of action which is approximately 150 Seconds if the object is stationary
and approximately 75 Seconds if the object is moving head on with the same speed as AUV.
Therefore it can be concluded that the algorithm proposed in this paper is aptly suited for the
application of obstacle Avoidance in case of AUV navigating in constrained underwater scenario.
7. ACKNOWLEDGEMENTS
The above work has been undertaken towards the research project of DRDO (NSTL). The authors
are thankful to the project team at NSTL for providing the SONAR data, constant technical
support and encouragement. Authors are also thankful to the management of their respective
organizations.
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77
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Authors
Smt Nagamani Modalavalasa received B.E. degree in Electronics and
Communication Engineering from Andhra University, Visakhapatnam, Andhra
Pradesh, India. She has completed her Master degree from Andhra University,
Visakhapatnam, India. At present she is a research scholar in Electronics &
Communication Engg. Department, JNTU Engg. College, Kakinada, Andhra
Pradesh, India. She has 15 years of teaching experience as Lecturer in the Department
of Electronics & Communication Engg, State Board of Technical Education &
Training, Andhra Pradesh, India. She has published around 15 research papers in
various International and National conferences.
Dr G Sasi Bhushana Rao received B E. degree in Electronics and Communication
Engineering from Andhra University College of Engineering, Visakhapatnam,
Andhra Pradesh, India, M.Tech. degree from JNTU, Hyderabad, India and Ph.D.
from Osmania University, Hyderabad, India. He possesses vast administration,
teaching and R&D experience at Airports Authority, Andhra University, India for
about 25 years. Currently he is working as Head Of Department in the Department of
Electronics & Communication Engg, Andhra University Engineering College,
Visakhapatnam, India. He has published more than 230 Technical and research
papers in different National / International conferences and Journals and authored two Text books. He has
guided 4 Ph.D. scholars and at present 14 scholars are working with him. His areas of Research include
Inertial Navigation System (INS), GPS/GNSS Signal processing, Ionosphere/Troposphere and Multipath
error modeling, and RADAR and SONAR navigation. Dr. Rao is a Fellow member of various professional
bodies like IEEE, IETE , IGU , and International GNSS society.
Dr. K. Satya Prasad received B Tech. degree in Electronics and Communication
Engineering from JNTU college of Engineering, Anantapur, Andhra Pradesh, India,
M.E. degree in Communication Systems from Guindy college of Engg. , Madras
University, Chennai, India and Ph. D from Indian Institute of Technology, Madras.
He has more than 31 years of experience in teaching and 23 years of R & D. He
started his teaching carrier as Teaching Assistant at Regional Engineering College,
Warangal in 1979. He joined JNT University, Hyderabad as Lecturer in 1980 and
served in different constituent college’s viz., Kakinada, Hyderabad and Anantapur and
at different capacities viz., Associate Professor, Professor, and Head of the
Department, Vice Principal and Principal. He has published more than 50 technical papers in different
National / International conferences and Journals and authored one Text book. He has guided 4 Ph.D.
scholars and at present 12 scholars are working with him. His areas of Research include Communications
Signal Processing, Image Processing, Speech Processing, Neural Networks & Ad-hoc wireless networks
etc. Dr. Prasad is a Fellow member of various professional bodies like IETE, IE, and ISTE.