This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main focus of this paper placed on the design of two main components (model classifier and inference engine) for a tool abbreviated as VASD (Video Action Scene Detector) for retrieving and detecting human actions from video scenes. The discussion starts by presenting the workflow of the retrieving and detection process and the automated model classifier construction logic. We then move on to demonstrate how the constructed classifiers can be used with multimodality features for detecting human actions. Finally, behavioral explanation manifestation is discussed. The simulator is implemented in bilingual; Math Lab and C++ are at the backend supplying data and theories while Java handles all front-end GUI and action pattern updating. To compare the usefulness of the proposed framework, several experiments were conducted and the results were obtained by using visual features only (77.89% for precision; 72.10% for recall), audio features only (62.52% for precision; 48.93% for recall) and combined audiovisual (90.35% for precision; 90.65% for recall).
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
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
The Basic Idea Behind “Smart Web Cam Motion Detection Surveillance System” Is To Stop The Intruder To Getting Into The Place Where A High End Security Is Required. This Paper Proposes A Method For Detecting The Motion Of A Particular Object Being Observed. The Motion Tracking Surveillance Has Gained A Lot Of Interests Over Past Few Years. This System Is Brought Into Effect Providing Relief To The Normal Video Surveillance System Which Offers Time-Consuming Reviewing Process. Through The Study And Evaluation Of Products, We Propose A Motion Tracking Surveillance System Consisting Of Its Method For Motion Detection And Its Own Graphic User Interface.
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.
HUMAN MOTION DETECTION AND TRACKING FOR VIDEO SURVEILLANCEAswinraj Manickam
An approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior.
This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence.
First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm.
A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language.
The group events recognition approach is successfully validated on 4 camera views from 3 data sets: an airport, a subway, a shopping center corridor and an entrance hall.
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
The Basic Idea Behind “Smart Web Cam Motion Detection Surveillance System” Is To Stop The Intruder To Getting Into The Place Where A High End Security Is Required. This Paper Proposes A Method For Detecting The Motion Of A Particular Object Being Observed. The Motion Tracking Surveillance Has Gained A Lot Of Interests Over Past Few Years. This System Is Brought Into Effect Providing Relief To The Normal Video Surveillance System Which Offers Time-Consuming Reviewing Process. Through The Study And Evaluation Of Products, We Propose A Motion Tracking Surveillance System Consisting Of Its Method For Motion Detection And Its Own Graphic User Interface.
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.
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.
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.
MOTION PREDICTION USING DEPTH INFORMATION OF HUMAN ARM BASED ON ALEXNETgerogepatton
The development of convolutional neural networks(CNN) has provided a new tool to make classification and prediction of human's body motion. This project tends to predict the drop point of a ball thrown out by experimenters by classifying the motion of their body in the process of throwing. Kinect sensor v2 is used to record depth maps and the drop points are recorded by a square infrared induction module. Firstly, convolutional neural networks are made use of to put the data obtained from depth maps in and get the prediction of drop point according to experimenters' motion. Secondly, huge amount of data is used to trainthe networks of different structure, and a network structure that could provide high enough accuracy for drop point prediction is established. The network model and parameters are modified to improve the accuracy of the prediction algorithm. Finally, the experimental data is divided into a training group and a test group. The prediction results of test group reflect that the prediction algorithm effectively improves the accuracy of human motion perception.
Detection and Tracking of Objects: A Detailed StudyIJEACS
Detecting and tracking objects are the most widespread and challenging tasks that a surveillance system must achieve to determine expressive events and activities, and automatically interpret and recover video content. An object can be a queue of people, a human, a head or a face. The goal of this article is to state the Detecting and tracking methods, classify them into different categories, and identify new trends, we introduce main trends and provide method to give a perception to fundamental ideas as well as to show their limitations in the object detection and tracking for more effective video analytics.
The main objective of this work is the uniting and streamlining of an automatic face detection application and recognition system for video indexing applications. Human identification means the classification of gender which can increase the identification accuracy. So, accurate gender classification algorithms may increase the accuracy of the applications and can reduce its complexity. But, in some applications, some challenges are there such as rotation, gray scale variations that may reduce the accuracy of the application. The main goal of building this module is to understand the values in image, pattern, and array processing with OpenCV for effective processing faces for building pipe-lining, SVM models.
Frequency based edge-texture feature using Otsu’s based enhanced local ternar...journalBEEI
Sharing information through images is a trend nowadays. Advancements in the technology and user-friendly image editing tool make easy to edit the image and spread fake news through different social networking platforms. Forged image has been generated through an advanced image editing tool, so it is very challenging for image forensics to detect the micro discrepancy which distorted the micro pattern. This paper proposes an image forensic detection technique, which implies multi-level discrete wavelet transform to implement digital image filtering. Canny edge detection technique is implemented to detect the edge of the image to implement Otsu’s based enhanced local ternary pattern (OELTP), which can detect forgery-related artifact. DWT is implemented over Cb and Cr components of the image and using edge texture to improve the Otsu global threshold, which is used to extract features using ELTP technique. Support vector machine (SVM) is used for classification to find the image is forged or not. The performance of the work evaluated on three different open available data sets CASIA v1, CASIA v2, and Columbia. Our proposed work gives better results with some of the previous states of the work in terms of detection accuracy.
A Critical Survey on Detection of Object and Tracking of Object With differen...Editor IJMTER
Basically object detection and object tracking are two important and challenging aspects in
many computer vision applications like surveillance system, vehicle navigation, autonomous robot
navigation, compression of video etc. Object detection is first low level important task for any video
surveillance application. To detection of moving object is a challenging task. Tracking is required in
higher level applications that required the location and shape of object. There are three key steps in
video analysis: detection of interesting moving objects, tracking of such objects from frame to frame,
and analysis of object tracks to recognize their behavior. Object detection and tracking especially for
human and vehicle is currently most active research topic. A lot of research has been undergoing
ranging from applications to noble algorithms. The main objective of this paper is to review (survey)
of various moving object detection and object tracking methodologies.
Java Implementation based Heterogeneous Video Sequence Automated Surveillance...CSCJournals
Automated video based surveillance monitoring is an essential and computationally challenging task to resolve issues in the secure access localities. This paper deals with some of the issues which are encountered in the integration surveillance monitoring in the real-life circumstances. We have employed video frames which are extorted from heterogeneous video formats. Each video frame is chosen to identify the anomalous events which are occurred in the sequence of time-driven process. Background subtraction is essentially required based on the optimal threshold and reference frame. Rest of the frames are ablated from reference image, hence all the foreground images paradigms are obtained. The co-ordinate existing in the deducted images is found by scanning the images horizontally until the occurrence of first black pixel. Obtained coordinate is twinned with existing co-ordinates in the primary images. The twinned co-ordinate in the primary image is considered as an active-region-of-interest. At the end, the starred images are converted to temporal video that scrutinizes the moving silhouettes of human behaviors in a static background. The proposed model is implemented in Java. Results and performance analysis are carried out in the real-life environments.
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.
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.
MOTION PREDICTION USING DEPTH INFORMATION OF HUMAN ARM BASED ON ALEXNETgerogepatton
The development of convolutional neural networks(CNN) has provided a new tool to make classification and prediction of human's body motion. This project tends to predict the drop point of a ball thrown out by experimenters by classifying the motion of their body in the process of throwing. Kinect sensor v2 is used to record depth maps and the drop points are recorded by a square infrared induction module. Firstly, convolutional neural networks are made use of to put the data obtained from depth maps in and get the prediction of drop point according to experimenters' motion. Secondly, huge amount of data is used to trainthe networks of different structure, and a network structure that could provide high enough accuracy for drop point prediction is established. The network model and parameters are modified to improve the accuracy of the prediction algorithm. Finally, the experimental data is divided into a training group and a test group. The prediction results of test group reflect that the prediction algorithm effectively improves the accuracy of human motion perception.
Detection and Tracking of Objects: A Detailed StudyIJEACS
Detecting and tracking objects are the most widespread and challenging tasks that a surveillance system must achieve to determine expressive events and activities, and automatically interpret and recover video content. An object can be a queue of people, a human, a head or a face. The goal of this article is to state the Detecting and tracking methods, classify them into different categories, and identify new trends, we introduce main trends and provide method to give a perception to fundamental ideas as well as to show their limitations in the object detection and tracking for more effective video analytics.
The main objective of this work is the uniting and streamlining of an automatic face detection application and recognition system for video indexing applications. Human identification means the classification of gender which can increase the identification accuracy. So, accurate gender classification algorithms may increase the accuracy of the applications and can reduce its complexity. But, in some applications, some challenges are there such as rotation, gray scale variations that may reduce the accuracy of the application. The main goal of building this module is to understand the values in image, pattern, and array processing with OpenCV for effective processing faces for building pipe-lining, SVM models.
Frequency based edge-texture feature using Otsu’s based enhanced local ternar...journalBEEI
Sharing information through images is a trend nowadays. Advancements in the technology and user-friendly image editing tool make easy to edit the image and spread fake news through different social networking platforms. Forged image has been generated through an advanced image editing tool, so it is very challenging for image forensics to detect the micro discrepancy which distorted the micro pattern. This paper proposes an image forensic detection technique, which implies multi-level discrete wavelet transform to implement digital image filtering. Canny edge detection technique is implemented to detect the edge of the image to implement Otsu’s based enhanced local ternary pattern (OELTP), which can detect forgery-related artifact. DWT is implemented over Cb and Cr components of the image and using edge texture to improve the Otsu global threshold, which is used to extract features using ELTP technique. Support vector machine (SVM) is used for classification to find the image is forged or not. The performance of the work evaluated on three different open available data sets CASIA v1, CASIA v2, and Columbia. Our proposed work gives better results with some of the previous states of the work in terms of detection accuracy.
A Critical Survey on Detection of Object and Tracking of Object With differen...Editor IJMTER
Basically object detection and object tracking are two important and challenging aspects in
many computer vision applications like surveillance system, vehicle navigation, autonomous robot
navigation, compression of video etc. Object detection is first low level important task for any video
surveillance application. To detection of moving object is a challenging task. Tracking is required in
higher level applications that required the location and shape of object. There are three key steps in
video analysis: detection of interesting moving objects, tracking of such objects from frame to frame,
and analysis of object tracks to recognize their behavior. Object detection and tracking especially for
human and vehicle is currently most active research topic. A lot of research has been undergoing
ranging from applications to noble algorithms. The main objective of this paper is to review (survey)
of various moving object detection and object tracking methodologies.
Java Implementation based Heterogeneous Video Sequence Automated Surveillance...CSCJournals
Automated video based surveillance monitoring is an essential and computationally challenging task to resolve issues in the secure access localities. This paper deals with some of the issues which are encountered in the integration surveillance monitoring in the real-life circumstances. We have employed video frames which are extorted from heterogeneous video formats. Each video frame is chosen to identify the anomalous events which are occurred in the sequence of time-driven process. Background subtraction is essentially required based on the optimal threshold and reference frame. Rest of the frames are ablated from reference image, hence all the foreground images paradigms are obtained. The co-ordinate existing in the deducted images is found by scanning the images horizontally until the occurrence of first black pixel. Obtained coordinate is twinned with existing co-ordinates in the primary images. The twinned co-ordinate in the primary image is considered as an active-region-of-interest. At the end, the starred images are converted to temporal video that scrutinizes the moving silhouettes of human behaviors in a static background. The proposed model is implemented in Java. Results and performance analysis are carried out in the real-life environments.
A NOVEL METHOD FOR PERSON TRACKING BASED K-NN : COMPARISON WITH SIFT AND MEAN...sipij
Object tracking can be defined as the process of detecting an object of interest from a video scene and
keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a
challenging problem and it’s an important task. Many researchers are getting attracted in the field of
computer vision, specifically the field of object tracking in video surveillance. The main purpose of this
paper is to give to the reader information of the present state of the art object tracking, together with
presenting steps involved in Background Subtraction and their techniques. In related literature we found
three main methods of object tracking: the first method is the optical flow; the second is related to the
background subtraction, which is divided into two types presented in this paper, then the temporal
differencing and the SIFT method and the last one is the mean shift method. We present a novel approach
to background subtraction that compare a current frame with the background model that we have set
before, so we can classified each pixel of the image as a foreground or a background element, then comes
the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is
divided into two different methods, the surface method and the K-NN method, both are explained in the
paper. Our proposed method is implemented and evaluated using CAVIAR database.
Abnormal activity detection in surveillance video scenesTELKOMNIKA JOURNAL
Automated detection of abnormal activity assumes a significant task in surveillance applications. This paper presents an intelligent framework video surveillance to detect abnormal human activity in an academic environment that takes into account the security and emergency aspects by focusing on three abnormal activities (falling, boxing and waving). This framework designed to consist of the two essential processes: the first one is a tracking system that can follow targets with identify sets of features to understand human activity and measure descriptive information of each target. The second one is a decision system that can realize if the activity of the target track is "normal" or "abnormal” then energizing alarm when recognized abnormal activities.
System analysis and design for multimedia retrieval systemsijma
Due to the extensive use of information technology and the recent developments in multimedia systems, the
amount of multimedia data available to users has increased exponentially. Video is an example of
multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio.
Content based video retrieval is an approach for facilitating the searching and browsing of large
multimedia collections over WWW. In order to create an effective video retrieval system, visual perception
must be taken into account. We conjectured that a technique which employs multiple features for indexing
and retrieval would be more effective in the discrimination and search tasks of videos. In order to validate
this, content based indexing and retrieval systems were implemented using color histogram, Texture feature
(GLCM), edge density and motion..
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
Proposed Multi-object Tracking Algorithm Using Sobel Edge Detection operatorQUESTJOURNAL
ABSTRACT:Tracking of moving objects that is called video tracking is used for measuring motion parameters and obtaining a visual record of the moving objects, it is an important area of application in image processing. In general there are two different approaches to obtain object tracking: the first is Recognition-based Tracking, and the second is the Motion-based Tracking. Video tracking system raises a wide possibility in today’s society. This system is used in various applications such as military, security, monitoring, robotic, and nowadays in dayto-day applications. However the video tracking systems still have many open problems and various research activities in a video tracking system are explores. This paper presents an algorithm for video tracking of any moving targets with the uses of contour based detection technique that depends on the sobel operator. The proposed system is suitable for indoor and outdoor applications. Our approach has the advantage of extending the applicability of tracking system and also, as presented here improves the performance of the tracker making feasible high frame rate video tracking. The goal of the tracking system is to analyze the video frames and estimate the position of a part of the input video frame (usually a moving object), our approach can detect, tracked any object more than one object and calculate the position of the moving objects. Therefore, the aim of this paper is to construct a motion tracking system for moving objects. Where, at the end of this paper, the detail outcome and result are discussed using experiments results of the proposed technique
Background Subtraction Algorithm Based Human Behavior DetectionIJERA Editor
Consider all the features of subset information in video streaming there is a tremendous processes with real time applications. In this paper we introduce and develop a new video surveillance system. Using this technique we detect human normal and exponential behaviors in realistic format, and also we categories data event generation of human tracking in real time applications. In this technique we apply differencing, threshold segmentation, morphological operations and object tracking. The experimental result show efficient human tracking in video streaming operations.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Overview on Edible Vaccine: Pros & Cons with Mechanism
A Framework for Human Action Detection via Extraction of Multimodal Features
1. Lili N. A.
International Journal of Image Processing (IJIP), Volume (3) : Issue (2) 73
A Framework for Human Action Detection via Extraction of
Multimodal Features
Lili N. A. liyana@fsktm.upm.edu.my
Department of Multimedia
Faculty of Computer Science & Information Technology
University Putra Malaysia
43400 UPM Serdang
Selangor, Malaysia
Abstract
This work discusses the application of an Artificial Intelligence technique called
data extraction and a process-based ontology in constructing experimental
qualitative models for video retrieval and detection. We present a framework
architecture that uses multimodality features as the knowledge representation
scheme to model the behaviors of a number of human actions in the video
scenes. The main focus of this paper placed on the design of two main
components (model classifier and inference engine) for a tool abbreviated as
VASD (Video Action Scene Detector) for retrieving and detecting human actions
from video scenes. The discussion starts by presenting the workflow of the
retrieving and detection process and the automated model classifier construction
logic. We then move on to demonstrate how the constructed classifiers can be
used with multimodality features for detecting human actions. Finally, behavioral
explanation manifestation is discussed. The simulator is implemented in bilingual;
Math Lab and C++ are at the backend supplying data and theories while Java
handles all front-end GUI and action pattern updating. To compare the
usefulness of the proposed framework, several experiments were conducted and
the results were obtained by using visual features only (77.89% for precision;
72.10% for recall), audio features only (62.52% for precision; 48.93% for recall)
and combined audiovisual (90.35% for precision; 90.65% for recall).
Keywords: audiovisual, human action detection, multimodal, hidden markov model.
1. INTRODUCTION
Action is the key content of all other contents in the video. Action recognition is a new technology
with many potential applications. Action recognition can be described as the analysis and
recognition of human motion patterns. Understanding activities of objects, especially humans,
moving in a scene by the use of video is both a challenging scientific problem and a very fertile
domain with many promising applications. Use of both audio and visual information to recognize
actions of human present might help to extract information that would improve the recognition
results. What to argue is that action is the key content of all other contents in the video. Just
imagine describing video content effectively without using a verb. A verb is just a description (or
expression) of actions. Action recognition will provide new methods to generate video retrieval
and categorization in terms of high-level semantics.
2. Lili N. A.
International Journal of Image Processing (IJIP), Volume (3) : Issue (2) 74
When either audio or visual information alone is not sufficient, combining audio and visual
features may resolve the ambiguities and to help to obtain more accurate answers Unlike the
traditional methods that analyze audio and video data separately, this research presents a
method which able to integrate audio and visual information for action scene analysis. The
approach is top-down for determining and extract action scenes in video by analyzing both audio
and visual data. A multidimensional layer framework was proposed to detect action scene
automatically. The first level extracts low level features such as motion, edge and colour to detect
video shots and next we use Hidden Markov model(HMM) to detect the action. An audio feature
vector consisting of n audio features which is computed over each short audio clip for the purpose
of audio segmentation was used too. Then it is time to decide the fusion process according to the
correspondences between the audio and the video scene boundaries using an HMM-based
statistical approach. Results are provided which prove the validity of the approach. The approach
consists of two stages: audiovisual event and semantic context detections. HMMs are used to
model basic audio events, and event detection is performed. Then semantic context detection is
achieved based on Gaussian mixture models, which model the correlations among several action
events temporally. With this framework, the gaps between low-level features and the semantic
contexts that last in a time series was bridged. The experimental evaluations indicate that the
approach is effective in detecting high-level semantics such as action scene.
2. RELATED WORKS
The problem of human action recognition is complicated by the complexity and variability of
shape and movement of the human body, which can be modelled as an articulated rigid body.
Moreover, two actions can occur simultaneously, e.g., walk and wave. Most work on action
recognition involving the full human body is concerned with actions completely described by
motion of the human body, i.e., without considering interactions with objects.
Previous works on audio visual content analysis were quite limited and still at a preliminary stage.
Most of the approaches are focused on visual information such as colour histogram differences,
motion vectors and key frames [3,4,5]. Colour histogram [7] difference and motion vector between
video frames or objects are the most common features in the scene recognition algorithms.
Although such features are quite successful in the video shot segmentation, scene detection
based on such visual features alone poses many problems. The extraction of relevant visual
features from an images sequence, and interpretation of this information for the purpose of
recognition and learning is a challenging task. An adequate choice of visual features is very
important for the success of action recognition systems [6].
Because of the different sets of genre classes and the different collections of video clips these
previous works chose, it is difficult to compare the performance of the different features and
approaches they used.
Automatic interpretation of human action in videos has been actively done for the past years. The
investigation of human motion has drawn a great attention from researches in computer vision or
computer graphics recently. Fruitful results can be found in many applications such as visual
surveillance. Features at different levels have been proposed for human activity analysis. Basic
image features based on motion histogram of objects are simple and reliable to compute (Efros et
al. 2003). In (Stauffer & Grimson 2000), a stable, real-time outdoor tracker is proposed, and high-
level classifications are based on blobs and trajectories output from this tracking system. In
(Zelnik-Manor & Irani 2001), dynamic actions are regarded as long-term temporal objects, and
spatio-temporal features at multiple temporal. There is a huge body of literature on the topics of
visual tracking, motion computation, and action detection. As tools and systems for producing and
disseminating action data improve significantly, the amount of human action detection system
grows rapidly. Therefore, an efficient approach to search and retrieve human action data is
needed.
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International Journal of Image Processing (IJIP), Volume (3) : Issue (2) 75
3. METHODOLOGY
The process of video action detection is depicted in Figure 1.0.
Figure 1.0 Metadata Generation Process
There are three classes of information. The main type of information, which serves as the input of
the complete system, is the binary level information, which comprises the raw video files. This
binary level information is analyzed which results in the so called feature level information, i.e.
features interesting for detecting certain action like the measurements of action speeds. Finally,
from this feature level information, actions are detected that are regarded as the concept level
information. When a user searches information from the multimedia, it does not have to browse
the binary level video files anymore, but it can directly query on concept level. The user can for
example query an action in which a man is punching another man, which might cause a violent
later on. As a result a more advanced search engine is created that can be used for human action
purposes.
In this paper, the feature extraction process (Figure 2.0) is discussed: converting the binary level
information coming from the video acquisition system into feature level information suited for
further processing. From the flowchart (Figure 3.0), we have four levels of processes:
• Pixel level represents the average percent of the changed pixels between frames within a
shot
• Histogram indicates the mean value of the histogram difference between frames within a
shot
• Segmentation level to indicate background and foreground areas
• Object tracking based on observation: flames, explosion, gun
Figure 2.0 The overview of the system
Present
ation
Concept
Level
Informati
on
Action
Recogni
tion and
Inferenc
e
Featur
e
Extracti
on
Binary Level Feature Leve lConcept Level
Video
Acquisi
tion
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International Journal of Image Processing (IJIP), Volume (3) : Issue (2) 76
Figure 3.0 Flow chart of feature extraction process
We proposed an action classification method with the following characteristics:
• Feature extraction is done automatically;
• The method deals with both visual and auditory information, and captures both spatial
and temporal characteristics;
• Edge feature extraction
• Motion
• Shot activity that conveys large amount of information about the type of video
• Colour feature extraction
• Sound
• The extracted features are natural, in the sense that they are closely related to the
human perceptual processing.
3.1 Segmentation
The segmentation and detection stages are often combined and simply called object detection.
Basically the task of the segmentation is to split the image into several regions based on color,
motion or texture information, whereas the detection stage has to choose relevant regions and
assign objects for further processing.
We follow the assumption that the video sequence is acquired using a stationary camera and
there is only very little background clutter. Based on this we use background differencing followed
by threshold to obtain a binary mask of the foreground region. In order to remove noise median
filtering and morphological operations are used. Regions of interest (ROI) are detected using
boundary extraction and a simple criterion based on the length of the boundary. Boundary filling
is applied to each ROI and the resulting binary object masks are given to the description stage.
3.2 Object Descriptor
The object description refers to a set of features that describe the detected object in terms of
color, shape, texture, motion, size etc. The goal of the feature extraction process is to reduce the
existing information in the image into a manageable amount of relevant properties. This leads to a
lower complexity and a more robust description. Additionally, the spatial arrangement of the
objects within the video frame and as related to each other is characterized by a topology.
A very important issue for the performance of a subsequent classification task is to select a
suitable descriptor that expresses both the similarity within a class and the distinctions between
different classes. Since the classifier strongly depends on the information provided by the
descriptor it is necessary to know its properties and limitations relating to this specific task. In
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International Journal of Image Processing (IJIP), Volume (3) : Issue (2) 77
case of human body posture recognition it is obvious that shape descriptors are needed to extract
useful information.
3.3 Classification
Classification is a pattern recognition (PR) problem of assigning an object to a class. Thus the
output of the PR system is an integer label. The task of the classifier is to partition the feature
space into class-labeled decision regions. Basically, classifiers can be divided into parametric and
non-parametric systems depending on whether they use statistical knowledge of the observation
and the corresponding class. A typical parametric system is the combination of Hidden Markov
Model (HMM) and Gaussian Mixture Model (GMM), which assumes Gaussian distribution of each
feature in the feature vector.
3.4 Posture and Gesture
Based on our overall system approach we treat the human body posture recognition as a basic
classification task. Given a novel binary object mask to be classified, and a database of samples
labeled with possible body postures, the previously described shape descriptors are extracted
and the image is classified with a chosen classifier. This can be interpreted as a database query:
given a query image, extract suitable descriptors and retrieve the best matching human body
posture label. Other similar queries are possible, resulting in a number of useful applications,
such as skeleton transfer, body posture synthesis and figure correction.
3.5 HMM and GMM
An approach for action recognition by using Hidden Markov Model (HMM) and Gaussian Mixture
Modelling (GMM) to model the video and audio streams respectively will be proposed. HMM will
be used to merge audio-visual information and to represent the hierarchical structure of the
particular violence activity. The visual features are used to characterize the type of shot view. The
audio features describe the audio events within a shot (scream, blast, gun shots). The edge,
motion (orientation and trajectory) information is then input to a HMM for recognition of the action.
Time series motion data of human’s whole body is used as input. Every category of target action
has a corresponding model (action model), and each action model independently calculates the
likelihood that the input data belongs to its category. Then the input motion is classified to the
most likely action category. The feature extraction (position, direction, movement) focuses
attention on the typical motion features of the action, and a model of the features’ behaviour in
the form of HMM.
A motion data is interpreted at various levels of abstraction. The HMM expresses what the action
is like by symbolic representation of time-series data. In this work, we combine information from
features that are based on image differences, audio differences, video differences, and motion
differences for feature extraction. Hidden Markov models provide a unifying framework for jointly
modeling these features. HMMs are used to build scenes from video which has already been
segmented into shots and transitions. States of the HMM consist of the various segments of a
video. The HMM contains arcs between states showing the allowable progressions of states. The
parameters of the HMM are learned using training data in the form of the frame-to-frame
distances for a video labeled with shots, transition types, and motion.
For each type of violent activity (punch and kick), a HMM will be build to characterize the action
and interaction processes as observation vectors. A HMM for each action is trained with the
corresponding training MPEG video sequences (kick, punch, run, walk, stand, etc.). The
expectation maximization (EM) and GMM approaches are then used to classify testing data using
the trained models. Once the mean and covariance of the Gaussian model of the training audio
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International Journal of Image Processing (IJIP), Volume (3) : Issue (2) 78
data are obtained, the likelihood ratio between the input audio track and the sound classes, is
computed to determine which class the associated sound belong to.
4. Experimental Discussion
By using these processes for action detection, the result of successful detected action is depicted
in Table 1. As is seen, sitting is characterized as the most significant motion with 80% of success
rate. This is due to less motion activities involved.
TABLE 1 Classification of the individual action sequences
Type of
Sequence
Total
Number
Correctly
Classified
%
Success
Standing 4 3 75
Sitting 5 4 80
Walking 3 2 67
Punching 5 3 60
Falling 4 3 75
Kicking 6 3 50
Running 2 1 50
These actions were taken from the dataset itself. For example, most of the standing and walking
scenes were collected from movie I, Robot. Most of the falling scenes were captured from Rush
Hour, sitting and punching from Charlie’s Angel, and kicking and running from Matrix. Figure 4
shows some scenes that demonstrate these actions for classification.
Figure 4.0 Some examples of video clips
5. Conclusion
In this research, we studied on techniques for extracting meaningful features that can be used to
extract higher level information from video shots. We will use motion, colour, and edge features,
sound and shot activity information to characterize the video data.
The proposed algorithm is implemented in C++ and it works on an Intel Pentium 2.56GHz
processor. As described above HMMs are trained from falling, walking, and walking and talking
video clips. A total of 64 video clips having 15,823 image frames are used. Some image frames
from the video clips are shown in Fig. 4. In all of the clips, only one moving object exists in the
scene.
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International Journal of Image Processing (IJIP), Volume (3) : Issue (2) 79
In summary, the main contribution of this work is the use of both audio and video tracks to decide
an action in video. The audio information is essential to distinguish an action from a person rather
than a person simply sitting down or sitting on a floor. To prove the usefulness of the proposed
method, the experiments were performed to evaluate the detection performance with several
video genres. The experimental results show that the proposed method to detect action scenes
gives high detection rate and reasonable processing time. The action detection time was
calculated for the case with the multimodal feature set. It takes about 102 seconds to detect
action within single video clip with PC (Pentium IV CPU 2.40GHz). The overall process time
depends on various factors: CPU clock speed, the type of language used in system
implementation, optimization scheme, the complexity and the number of processing steps, etc.
Because the proposed action detection is performed with unit of short video clip, the detection
time is not affected by the length of entire video.
6. References
1. L. Zelnik-Manor and M. Irani, “Event-based Analysis of Video”. Proceedings of IEEE Conference
Computer Vision and Pattern Recognition, 2001.
2. C. Stauffer and W.E.L. Grimson, “Learning Patterns of Activities using Real-Time Tracking”. Journal of
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol (22), no. (8), pp. 747 – 757, 2001.
3. A.A. Efros, A.C. Berg, G. Mori and J. Malik, “Recognizing Action at a Distance”. Proceedings of
International Conference on Computer Vision, 2003.
4. S. Fischer, R. Lienhart and W. Effelsberg, “Automatic Recognition of Film Genres”, Proceedings of ACM
Multimedia, pp. 295 – 304, 2003.
5. G. Tzanetakis and P. Cook, “Musical Genre Classification of Audio Signals”, IEEE Trans. On Speech
and Audio Processing, vol. 10, no. 5, pp. 293 – 302, 2002.
6. C.P., Tan, K.S. Lim, and W.K. Lai. 2008. Multi-Dimensional Features Reduction of Consistency Subset
Evaluator on Unsupervised Expectation Maximization Classifier for Imaging Surveillance Application.
International Journal of Image Processing, vol. 2(1), pp. 18-26.
7. J. P and P.S. Hiremath. 2008. Content Based Image Retrieval using Color Boosted Salient Points and
Shape features of an image. 2008. International Journal of Image Processing, vol. 2(1), pp. 10-17.