The document provides an overview of a hybrid approach for human posture recognition using a 3D avatar model and 2D silhouette comparisons. The approach generates 3D posture avatars from a human body model, extracts silhouettes from different viewpoints, and compares the silhouettes to detected poses using various 2D representation techniques. It was evaluated on both synthetic and real video data and was able to correctly recognize general and detailed postures in near real-time. Future work is focused on improving computation time and handling occlusion.
This document proposes using gait as a biometric for human identification. It discusses extracting image features from a person's silhouette and gait cycle, including the width of their outer contour and their entire binary silhouette. Two approaches are explored: the indirect approach transforms these high-dimensional image features into lower dimensions before using an HMM for representation, while the direct approach uses the features directly with an HMM. The document outlines issues with existing identification systems and argues that gait recognition could provide a more unique, reliable means of identification than alternatives like signatures or facial recognition.
This document summarizes research on human motion tracking techniques using skeleton models. It discusses how model-based approaches use a predefined human skeleton model to represent joints and segments. Video-based approaches can infer physical attributes and daily actions without sensors. The document reviews several papers on reconstructing 3D human pose from video, using reduced joint sets and shape models to filter noise and track landmarks, and developing multi-view pose tracking using generative sampling and physical constraints. It also discusses challenges like high degrees of freedom and self-occlusion, and the need for efficient algorithms to enable real-time 3D full-body motion tracking from multiple cameras.
A Video System for Measuring School Children Sitting Posture DynamicsWaqas Tariq
School children spent a lot of time sitting. Some Primary Schools in Slovenia were interested to improve pupil’s working conditions by introducing more dynamic type of sitting. A standard school chair was substituted with a large gymnastic ball. In order to evaluate influence of this substitution on sitting dynamics we developed a video system capable of assessing sitting posture in sagittal plain during prolonged period.
We composed a video acquisition system with video camera (Blaupunkt, CCR 808), simple optical markers with LED diodes and robust image analysing software. To test it we measured the sitting posture of eight school children, who were sitting for 30 minutes on a large gymnastic ball and on a chair without a backrest and armrest with the acquisition rate 3 s -1. Each image was analysed to determine position of markers and then the Lumbar Lordosis angle (LL) and the Pelvis Inclination angle (PI) time courses were calculated.
We found a measurement system very convenient in the conditions outside the laboratory. The level of backscatter which could impair automatic marker location extraction from the recorded image was low during all sessions. The marker in the recorded image had 30±10 pixels with different intensity. We found that during first 6 minutes the posture is more upright on the ball as compared to the chair (PI: chair 17.0±7.2, ball 13.2 ±8.5, p<0.05;>0.05).
A measurement system using consumer video camera, LED video markers and image analytics software is cost effective and reliable system which has minimal influence on students comfort during measurements outside the laboratory.
The document discusses different approaches to abnormal behavior recognition: supervised, unsupervised, and semi-supervised. The supervised approach relies on clearly labeled normal and abnormal data but abnormal behavior is rare and undefined. Unsupervised approaches either cluster patterns or build a database of normal behavior patterns to detect abnormalities. Semi-supervised uses labeled normal data to build a normal model and then learns the abnormal model unsupervised. All approaches still have issues like insufficient labeled data or inconsistent manual labeling.
Learning target Pattern-of-Life for wide-area Anomaly Detectionzepolitat
The document presents a methodology for learning pattern-of-life (POL) models from GPS tracking data to detect anomalies. It proposes a hierarchical model with temporal and spatial layers. The temporal layer models preferred schedules using kernel density estimation (KDE) and conformal anomaly detection. The spatial layer models preferred routes using online clustering. The methodology is tested on two datasets and detects spatial and spatiotemporal anomalies. Results show CAD detects narrower anomalies than KDE. Future work includes improving the models and testing other techniques.
Within this media OTS, the framing is effective as it draws the viewer's eye to important elements like a doll hanging from the ceiling, implying a suicide or murder. Sound is also used well, with clock ticks and voices narrating the story in a doll house setting. However, the video could be improved by adding more varied transitions beyond just fades, including different shot distances beyond just close-ups, and designing title credits that better fit the genre. Based on these elements, the group would receive a grade of D, at Level 2.
This document proposes using gait as a biometric for human identification. It discusses extracting image features from a person's silhouette and gait cycle, including the width of their outer contour and their entire binary silhouette. Two approaches are explored: the indirect approach transforms these high-dimensional image features into lower dimensions before using an HMM for representation, while the direct approach uses the features directly with an HMM. The document outlines issues with existing identification systems and argues that gait recognition could provide a more unique, reliable means of identification than alternatives like signatures or facial recognition.
This document summarizes research on human motion tracking techniques using skeleton models. It discusses how model-based approaches use a predefined human skeleton model to represent joints and segments. Video-based approaches can infer physical attributes and daily actions without sensors. The document reviews several papers on reconstructing 3D human pose from video, using reduced joint sets and shape models to filter noise and track landmarks, and developing multi-view pose tracking using generative sampling and physical constraints. It also discusses challenges like high degrees of freedom and self-occlusion, and the need for efficient algorithms to enable real-time 3D full-body motion tracking from multiple cameras.
A Video System for Measuring School Children Sitting Posture DynamicsWaqas Tariq
School children spent a lot of time sitting. Some Primary Schools in Slovenia were interested to improve pupil’s working conditions by introducing more dynamic type of sitting. A standard school chair was substituted with a large gymnastic ball. In order to evaluate influence of this substitution on sitting dynamics we developed a video system capable of assessing sitting posture in sagittal plain during prolonged period.
We composed a video acquisition system with video camera (Blaupunkt, CCR 808), simple optical markers with LED diodes and robust image analysing software. To test it we measured the sitting posture of eight school children, who were sitting for 30 minutes on a large gymnastic ball and on a chair without a backrest and armrest with the acquisition rate 3 s -1. Each image was analysed to determine position of markers and then the Lumbar Lordosis angle (LL) and the Pelvis Inclination angle (PI) time courses were calculated.
We found a measurement system very convenient in the conditions outside the laboratory. The level of backscatter which could impair automatic marker location extraction from the recorded image was low during all sessions. The marker in the recorded image had 30±10 pixels with different intensity. We found that during first 6 minutes the posture is more upright on the ball as compared to the chair (PI: chair 17.0±7.2, ball 13.2 ±8.5, p<0.05;>0.05).
A measurement system using consumer video camera, LED video markers and image analytics software is cost effective and reliable system which has minimal influence on students comfort during measurements outside the laboratory.
The document discusses different approaches to abnormal behavior recognition: supervised, unsupervised, and semi-supervised. The supervised approach relies on clearly labeled normal and abnormal data but abnormal behavior is rare and undefined. Unsupervised approaches either cluster patterns or build a database of normal behavior patterns to detect abnormalities. Semi-supervised uses labeled normal data to build a normal model and then learns the abnormal model unsupervised. All approaches still have issues like insufficient labeled data or inconsistent manual labeling.
Learning target Pattern-of-Life for wide-area Anomaly Detectionzepolitat
The document presents a methodology for learning pattern-of-life (POL) models from GPS tracking data to detect anomalies. It proposes a hierarchical model with temporal and spatial layers. The temporal layer models preferred schedules using kernel density estimation (KDE) and conformal anomaly detection. The spatial layer models preferred routes using online clustering. The methodology is tested on two datasets and detects spatial and spatiotemporal anomalies. Results show CAD detects narrower anomalies than KDE. Future work includes improving the models and testing other techniques.
Within this media OTS, the framing is effective as it draws the viewer's eye to important elements like a doll hanging from the ceiling, implying a suicide or murder. Sound is also used well, with clock ticks and voices narrating the story in a doll house setting. However, the video could be improved by adding more varied transitions beyond just fades, including different shot distances beyond just close-ups, and designing title credits that better fit the genre. Based on these elements, the group would receive a grade of D, at Level 2.
Robust techniques for background subtraction in urbantaylor_1313
Robust techniques for background subtraction in urban traffic video aim to identify moving objects from video sequences. The paper surveys and compares various background subtraction algorithms, including simple techniques like frame differencing and adaptive median filtering, as well as more sophisticated probabilistic modeling. Experiments show that while complex techniques often perform best, simple adaptive median filtering produces good results with much lower computational complexity for detecting vehicles and pedestrians in traffic video.
This document discusses anomaly detection in human crowds using dynamic motion vector modeling. It outlines detecting unwanted objects, actions, or motion behaviors in crowds to avoid hazardous situations or catch rule breakers. Current methods like surveillance operators are limited, so the goal is to develop an automated system. The proposed method models optical flow over time to track pixel trajectories dynamically. This allows modeling crowd motion, but faces challenges from occlusion, noisy optical flow, and shadows. Examples show segmenting normal motion models and detecting anomalies. Future work includes making the system more robust by modeling larger homogeneous regions.
Ensemble of Exemplar-SVM for Object Detection and Beyondzukun
The document summarizes research on an Ensemble of Exemplar-SVMs approach for object detection and other tasks. It trains a separate linear SVM for each exemplar or training example, allowing features to be tailored to each exemplar. This combines benefits of discriminative training and nearest neighbor methods. It outperforms other approaches on PASCAL VOC object detection and shows promise for tasks like geometry transfer and person prediction. The key is large-scale negative mining during Exemplar-SVM training.
Seminar on Driver Behaviour Detection using Swarm Intelligence.Rajani Suryavanshi
This document presents an approach for context-aware driver behavior detection using pervasive computing. It aims to reduce road accidents caused by driver errors by alerting drivers in a timely manner. The approach uses a three-tier network to gather context data from sensors using wireless sensor networks. Swarm intelligence and ant colony optimization are then used to infer driver behavior from the collected context data and detect unacceptable behaviors like fatigue or intoxication. The approach integrates wireless sensor networks, vehicle ad hoc networks, and swarm intelligence for comprehensive and reliable driver behavior monitoring.
Current state of art contains several methods to achieve intelligent tracking. Some methods are machine learning oriented. In these methods, activities are learnt from the context in an unsupervised or semi supervised manner. One other method is description based event recognition. In the heart of the method , describing scenarios wrt activities employed. For the description, a language is necessarily needed. There are mathematical languages in which logic is used to represent activities and their relations.Also some graphical languages such as hidden markov models, state machines, state charts are being used. Some textual languages proposed as well.
This presentation was prepared by Ishara Amarasekera based on the paper, Activity Recognition using Cell Phone Accelerometers by Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore.
This presentation contains a summary of the content provided in this research paper and was presented as a paper discussion for the course, Mobile and Ubiquitous Application Development in Computer Science.
The document discusses human action recognition using spatio-temporal features. It proposes using optical flow and shape-based features to form motion descriptors, which are then classified using Adaboost. Targets are localized using background subtraction. Optical flows within localized regions are organized into a histogram to describe motion. Differential shape information is also captured. The descriptors are used to train a strong classifier with Adaboost that can recognize actions in testing videos.
The document discusses human activity recognition from video data using computer vision techniques. It describes recognizing activities at different levels from object locations to full activities. Basic activities like walking and clapping are the focus. Key steps involve tracking segmented objects across frames and comparing motion patterns to templates to identify activities through model fitting. The DEV8000 development kit and Linux are used to process video and recognize activities in real-time. Applications discussed include surveillance, sports analysis, and unmanned vehicles.
This document summarizes a student project on human activity recognition using smartphones. A group of 4 students submitted the project to partially fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The project involved developing a system to recognize human activities using the accelerometer and gyroscope sensors in smartphones. Various machine learning algorithms were tested and evaluated on experimental data collected from smartphone sensors. The goal of the project was to create an accurate and lightweight activity recognition system for smartphones, while also exploring active learning methods to reduce the amount of labeled training data needed.
The document discusses epidemiological surveillance systems. It defines surveillance as the systematic collection and analysis of health data to understand disease patterns and control diseases. The objectives of surveillance include monitoring disease trends, identifying outbreaks, and informing public health policies. Effective surveillance requires defining conditions of interest, collecting standardized data, analyzing trends over time and place, and disseminating findings to decision-makers.
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.
The document provides an overview of psychological disorders including:
- Historical models including the supernatural model which saw disorders as possession and the natural/medical model which saw them as diseases.
- Modern models including psychoanalytic, biological, cognitive-behavioral, and diathesis-stress.
- The DSM-IV-TR classification system and descriptions of mood disorders like depression and mania, anxiety disorders, psychosomatic disorders, dissociative disorders, sexual/gender disorders, and personality disorders.
- Personality disorders are grouped into three clusters characterized by odd/eccentric, dramatic/erratic, and anxious/fearful behaviors respectively. Common disorders are described within each cluster.
1) The document discusses surveillance in public health and describes its key components and purposes. Surveillance involves the systematic collection, analysis, and interpretation of health data to provide information for action.
2) An effective surveillance system is simple, flexible, timely, and produces high-quality data. It addresses an important public health problem and accomplishes its objectives of understanding disease trends, detecting outbreaks, and evaluating control measures.
3) The document outlines how to establish a surveillance system, including selecting priority diseases, defining standard case definitions, and developing regular reporting and data dissemination processes. Both passive and active surveillance methods are described.
Object tracking involves tracing the movement of objects in a video sequence. There are various object representation methods like points, shapes, and skeletons. Popular tracking algorithms include point tracking, kernel tracking, and silhouette tracking. Key steps are object detection, feature extraction, segmentation, and tracking. Common challenges are illumination changes, occlusions, and complex motions. The document compares methods like optical flow, mean shift, and feature-based tracking. In conclusion, object tracking has advanced but challenges remain like handling occlusions.
Video object tracking with classification and recognition of objectsManish Khare
The document discusses an ongoing research project on video object tracking using classification and recognition. It presents the initial progress made, including work on automatic image segmentation using level set methods and detection/removal of shadows. Level set methods allow flexible representation of object contours and boundaries during segmentation. The research aims to automatically track and classify multiple objects in video sequences.
This document summarizes a research paper on detecting and tracking human motion based on background subtraction. The proposed method initializes the background using the median of multiple frames. It then extracts moving objects by subtracting the current frame from the background and applying a dynamic threshold. Noise is removed using filters and morphology operations. Shadows are accounted for using projection analysis to accurately detect human bodies. Tracking involves computing the centroid of detected objects in each frame to analyze position and velocity over time. Experimental results showed the method runs quickly and accurately for real-time detection of human motion.
Human Re-identification using Soft Biometrics in Video SurveillanceShengzhe Li
Ph.D. defense on "human re-identification using soft biometrics in video surveillance". The presentation includes two parts:
Part 1: simplified camera calibration
Part 2: human re-identification using soft biometrics
1. The document describes a real-time detector for unusual behavior that uses motion and shape-based analysis to detect statistically relevant unusual events in video and alert users.
2. It outlines the contributions and responsibilities of different partners - ACV focuses on motion detection and tracking, Bilkent on human detection and action recognition, UPC on foreground detection and body modeling, and SZTAKI on unusual event detection and the software platform.
3. The system will integrate computer vision techniques like foreground detection, tracking, human detection, action recognition and motion-based unusual event detection to process video streams and detect anomalous behaviors in real-time.
This document discusses human gait analysis for forensic purposes. It defines key gait terms like footprint, gait pattern, gait cycle and their analysis. The gait cycle has two phases - stance and swing. Gait features like gait line, foot angle, step length are examined. Factors influencing gait and methods of gait recognition from video are described. The document also outlines a gait-based person verification system for forensics using subject registration, silhouette creation and gait verification modules. This system allows objective gait analysis without expert knowledge for criminal identification.
This document discusses robot vision systems. It covers topics like industrial robotics, medical robotics, computer vision capabilities for robotics like object recognition and registration, vision sensors, issues with vision systems, and visual servoing techniques. Application examples discussed include using vision for accurate robot positioning, laparoscopic surgery, and tracking instruments.
The document discusses a system for classifying human actions in videos. It tracks subjects using adaptive background subtraction and extracts their bounding boxes. It then recognizes poses within each frame using Histogram of Oriented Gradients (HOG) templates. It also maintains a queue of the last K frames to classify actions based on sequences of poses over multiple frames. The system was tested on videos of judo matches and exercises performed by the authors.
Robust techniques for background subtraction in urbantaylor_1313
Robust techniques for background subtraction in urban traffic video aim to identify moving objects from video sequences. The paper surveys and compares various background subtraction algorithms, including simple techniques like frame differencing and adaptive median filtering, as well as more sophisticated probabilistic modeling. Experiments show that while complex techniques often perform best, simple adaptive median filtering produces good results with much lower computational complexity for detecting vehicles and pedestrians in traffic video.
This document discusses anomaly detection in human crowds using dynamic motion vector modeling. It outlines detecting unwanted objects, actions, or motion behaviors in crowds to avoid hazardous situations or catch rule breakers. Current methods like surveillance operators are limited, so the goal is to develop an automated system. The proposed method models optical flow over time to track pixel trajectories dynamically. This allows modeling crowd motion, but faces challenges from occlusion, noisy optical flow, and shadows. Examples show segmenting normal motion models and detecting anomalies. Future work includes making the system more robust by modeling larger homogeneous regions.
Ensemble of Exemplar-SVM for Object Detection and Beyondzukun
The document summarizes research on an Ensemble of Exemplar-SVMs approach for object detection and other tasks. It trains a separate linear SVM for each exemplar or training example, allowing features to be tailored to each exemplar. This combines benefits of discriminative training and nearest neighbor methods. It outperforms other approaches on PASCAL VOC object detection and shows promise for tasks like geometry transfer and person prediction. The key is large-scale negative mining during Exemplar-SVM training.
Seminar on Driver Behaviour Detection using Swarm Intelligence.Rajani Suryavanshi
This document presents an approach for context-aware driver behavior detection using pervasive computing. It aims to reduce road accidents caused by driver errors by alerting drivers in a timely manner. The approach uses a three-tier network to gather context data from sensors using wireless sensor networks. Swarm intelligence and ant colony optimization are then used to infer driver behavior from the collected context data and detect unacceptable behaviors like fatigue or intoxication. The approach integrates wireless sensor networks, vehicle ad hoc networks, and swarm intelligence for comprehensive and reliable driver behavior monitoring.
Current state of art contains several methods to achieve intelligent tracking. Some methods are machine learning oriented. In these methods, activities are learnt from the context in an unsupervised or semi supervised manner. One other method is description based event recognition. In the heart of the method , describing scenarios wrt activities employed. For the description, a language is necessarily needed. There are mathematical languages in which logic is used to represent activities and their relations.Also some graphical languages such as hidden markov models, state machines, state charts are being used. Some textual languages proposed as well.
This presentation was prepared by Ishara Amarasekera based on the paper, Activity Recognition using Cell Phone Accelerometers by Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore.
This presentation contains a summary of the content provided in this research paper and was presented as a paper discussion for the course, Mobile and Ubiquitous Application Development in Computer Science.
The document discusses human action recognition using spatio-temporal features. It proposes using optical flow and shape-based features to form motion descriptors, which are then classified using Adaboost. Targets are localized using background subtraction. Optical flows within localized regions are organized into a histogram to describe motion. Differential shape information is also captured. The descriptors are used to train a strong classifier with Adaboost that can recognize actions in testing videos.
The document discusses human activity recognition from video data using computer vision techniques. It describes recognizing activities at different levels from object locations to full activities. Basic activities like walking and clapping are the focus. Key steps involve tracking segmented objects across frames and comparing motion patterns to templates to identify activities through model fitting. The DEV8000 development kit and Linux are used to process video and recognize activities in real-time. Applications discussed include surveillance, sports analysis, and unmanned vehicles.
This document summarizes a student project on human activity recognition using smartphones. A group of 4 students submitted the project to partially fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The project involved developing a system to recognize human activities using the accelerometer and gyroscope sensors in smartphones. Various machine learning algorithms were tested and evaluated on experimental data collected from smartphone sensors. The goal of the project was to create an accurate and lightweight activity recognition system for smartphones, while also exploring active learning methods to reduce the amount of labeled training data needed.
The document discusses epidemiological surveillance systems. It defines surveillance as the systematic collection and analysis of health data to understand disease patterns and control diseases. The objectives of surveillance include monitoring disease trends, identifying outbreaks, and informing public health policies. Effective surveillance requires defining conditions of interest, collecting standardized data, analyzing trends over time and place, and disseminating findings to decision-makers.
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.
The document provides an overview of psychological disorders including:
- Historical models including the supernatural model which saw disorders as possession and the natural/medical model which saw them as diseases.
- Modern models including psychoanalytic, biological, cognitive-behavioral, and diathesis-stress.
- The DSM-IV-TR classification system and descriptions of mood disorders like depression and mania, anxiety disorders, psychosomatic disorders, dissociative disorders, sexual/gender disorders, and personality disorders.
- Personality disorders are grouped into three clusters characterized by odd/eccentric, dramatic/erratic, and anxious/fearful behaviors respectively. Common disorders are described within each cluster.
1) The document discusses surveillance in public health and describes its key components and purposes. Surveillance involves the systematic collection, analysis, and interpretation of health data to provide information for action.
2) An effective surveillance system is simple, flexible, timely, and produces high-quality data. It addresses an important public health problem and accomplishes its objectives of understanding disease trends, detecting outbreaks, and evaluating control measures.
3) The document outlines how to establish a surveillance system, including selecting priority diseases, defining standard case definitions, and developing regular reporting and data dissemination processes. Both passive and active surveillance methods are described.
Object tracking involves tracing the movement of objects in a video sequence. There are various object representation methods like points, shapes, and skeletons. Popular tracking algorithms include point tracking, kernel tracking, and silhouette tracking. Key steps are object detection, feature extraction, segmentation, and tracking. Common challenges are illumination changes, occlusions, and complex motions. The document compares methods like optical flow, mean shift, and feature-based tracking. In conclusion, object tracking has advanced but challenges remain like handling occlusions.
Video object tracking with classification and recognition of objectsManish Khare
The document discusses an ongoing research project on video object tracking using classification and recognition. It presents the initial progress made, including work on automatic image segmentation using level set methods and detection/removal of shadows. Level set methods allow flexible representation of object contours and boundaries during segmentation. The research aims to automatically track and classify multiple objects in video sequences.
This document summarizes a research paper on detecting and tracking human motion based on background subtraction. The proposed method initializes the background using the median of multiple frames. It then extracts moving objects by subtracting the current frame from the background and applying a dynamic threshold. Noise is removed using filters and morphology operations. Shadows are accounted for using projection analysis to accurately detect human bodies. Tracking involves computing the centroid of detected objects in each frame to analyze position and velocity over time. Experimental results showed the method runs quickly and accurately for real-time detection of human motion.
Human Re-identification using Soft Biometrics in Video SurveillanceShengzhe Li
Ph.D. defense on "human re-identification using soft biometrics in video surveillance". The presentation includes two parts:
Part 1: simplified camera calibration
Part 2: human re-identification using soft biometrics
1. The document describes a real-time detector for unusual behavior that uses motion and shape-based analysis to detect statistically relevant unusual events in video and alert users.
2. It outlines the contributions and responsibilities of different partners - ACV focuses on motion detection and tracking, Bilkent on human detection and action recognition, UPC on foreground detection and body modeling, and SZTAKI on unusual event detection and the software platform.
3. The system will integrate computer vision techniques like foreground detection, tracking, human detection, action recognition and motion-based unusual event detection to process video streams and detect anomalous behaviors in real-time.
This document discusses human gait analysis for forensic purposes. It defines key gait terms like footprint, gait pattern, gait cycle and their analysis. The gait cycle has two phases - stance and swing. Gait features like gait line, foot angle, step length are examined. Factors influencing gait and methods of gait recognition from video are described. The document also outlines a gait-based person verification system for forensics using subject registration, silhouette creation and gait verification modules. This system allows objective gait analysis without expert knowledge for criminal identification.
This document discusses robot vision systems. It covers topics like industrial robotics, medical robotics, computer vision capabilities for robotics like object recognition and registration, vision sensors, issues with vision systems, and visual servoing techniques. Application examples discussed include using vision for accurate robot positioning, laparoscopic surgery, and tracking instruments.
The document discusses a system for classifying human actions in videos. It tracks subjects using adaptive background subtraction and extracts their bounding boxes. It then recognizes poses within each frame using Histogram of Oriented Gradients (HOG) templates. It also maintains a queue of the last K frames to classify actions based on sequences of poses over multiple frames. The system was tested on videos of judo matches and exercises performed by the authors.
Comparison of Some Motion Detection Methods in cases of Single and Multiple M...CSCJournals
Motion detection tells us whether there is a change in position of an object with respect to its surroundings or vice versa. It is applied to various domestic and commercial applications starting from simple motion detectors to high speed video surveillance systems. In this paper, results obtained from some simple motion detection algorithms, which use methods like image subtraction and edge detection, have been compared. The software used for this purpose was MATLAB 7.6.0 (R2008a). It has been observed that while image subtraction is sufficient to detect motion in a video stream, combining it with edge detection in different sequences yields different results in different scenarios.
A Comparison of People Counting Techniques viaVideo Scene AnalysisPoo Kuan Hoong
Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document discusses quality assessment of 2D and 3D images. It defines image quality assessment as creating algorithms to gauge the perceived quality of visual stimuli as judged by human observers. There are two types of quality assessment: objective and subjective. Objective algorithms are categorized as full-reference, no-reference, and reduced-reference. The document outlines various parameters for 2D image quality assessment and discusses how 3D images are created using depth cues. Popular 3D quality metrics like SSIM and UQI are also mentioned.
1. The document discusses gait recognition from video for biometric identification. It provides background on biometric recognition and discusses gait as an identifying biometric trait that can be captured from a distance.
2. Various research approaches to gait recognition are covered, including model-based, motion-based, and mixed approaches. Commonly used gait recognition databases are also listed.
3. Recent works applying techniques like matrix representations, Bayesian frameworks, and symmetry-based detection are summarized, demonstrating applications in human identification, activity recognition, and scene registration. Future directions discussed include improving performance under more natural conditions.
The document discusses object tracking in computer vision. It begins with an introduction and overview of applications of object tracking. It then discusses object representation, detection, tracking algorithms and methodologies. It compares different tracking methods and provides an example of object tracking in MATLAB. Key steps in object tracking include object detection, tracking the detected objects across frames using algorithms like point tracking, kernel tracking and silhouette tracking. Common challenges with object tracking are also summarized.
Gait Recognition using MDA, LDA, BPNN and SVMIJEEE
Recognition of any individual is a task to identify the human beings. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot of humans. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. In this paper it will present the review of gait recognition system where different approaches and classification categories of Gait recognition like model free and model based approach, MDA, BPNN, LDA, and SVM.
Robust Human Tracking Method Based on Apperance and Geometrical Features in N...csandit
This paper proposes a robust tracking method which concatenates appearance and geometrical
features to re-identify human in non-overlapping views. A uniformly-partitioning method is
proposed to extract local HSV(Hue, Saturation, Value) color features in upper and lower
portion of clothing. Then adaptive principal view selecting algorithm is presented to locate
principal view which contains maximum appearance feature dimensions captured from different
visual angles. For each appearance feature dimension in principal view, all its inner frames get
involved in training a support vector machine (SVM). In matching process, human candidate
filtering is first operated with an integrated geometrical feature which connects height estimate
with gait feature. The appearance features of the remaining human candidates are later tested
by SVMs to determine the object’s existence in new cameras. Experimental results show the
feasibility and effectiveness of this proposal and demonstrate the real-time in appearance
feature extraction and robustness to illumination and visual angle change.
ROBUST HUMAN TRACKING METHOD BASED ON APPEARANCE AND GEOMETRICAL FEATURES IN ...cscpconf
This paper proposes a robust tracking method which concatenates appearance and geometrical
features to re-identify human in non-overlapping views. A uniformly-partitioning method is
proposed to extract local HSV(Hue, Saturation, Value) color features in upper and lower
portion of clothing. Then adaptive principal view selecting algorithm is presented to locate
principal view which contains maximum appearance feature dimensions captured from different
visual angles. For each appearance feature dimension in principal view, all its inner frames get
involved in training a support vector machine (SVM). In matching process, human candidate
filtering is first operated with an integrated geometrical feature which connects height estimate
with gait feature. The appearance features of the remaining human candidates are later tested
by SVMs to determine the object’s existence in new cameras. Experimental results show the
feasibility and effectiveness of this proposal and demonstrate the real-time in appearance
feature extraction and robustness to illumination and visual angle change.
This document summarizes object tracking methods, including representations of objects, features for tracking, detection approaches, tracking algorithms, and future directions. It discusses representing objects as points, patches, or contours, using features like color, edges, texture, and optical flow for detection and tracking. Detection can be done through point detection, background subtraction, segmentation, and supervised learning. Tracking algorithms include point tracking, kernel tracking, and silhouette tracking. The document outlines challenges like occlusion, camera motion, and non-rigid objects that remain for future work in object tracking.
This document proposes a system to measure human movement speed and distance from a camera based on analyzing interocular distance. It detects eye position, calculates the distance between the eyes (interocular distance), and uses this to measure the distance from the person to the camera and their movement speed in real-time. The system was tested and achieved 94.11% accuracy in measuring person-to-camera distance. Future work could involve improving accuracy for faces at different angles and considering height, weight, and 3D interocular distance.
An approach for human gait identification based on areaIOSR Journals
This document presents a new approach for human gait identification based on calculating the area of a triangle formed between three dynamic body parameters: left hand, right foot, and left foot. Video frames of walking subjects are analyzed to extract these three points in each frame. A triangle is formed and its area is calculated using Heron's formula. The mean area value over multiple frames is stored in a database for each subject. When tested on the CASIA gait dataset, the method achieved an average recognition rate of 88% by comparing the mean area values of unknown subjects to those in the database.
6. Applications Human Behaviour analysis Human posture recognition Video surveillance systems Aware house applications Virtual reality Intelligent user interfaces Sport monitoring
14. Previous work – video sensor Low processing time Independent from the viewpoint Hybrid approach = 2D approaches + 3D approaches High processing time Dependence from the viewpoint Drawbacks Independent from the viewpoint Low processing time Advantages 3D approaches 2D approaches
29. Silhouette Comparison Classification of 2D methods to represent silhouettes --- + Distance transform -- --- Shape from context -- + Skeletonisation - ++ H. & V. projections -- ++ Geometric features -- ++ Hu moments Independence from the silhouette quality Computation rapidity 2D methods
44. Real video – own sequences Current image Binary image Detailed postures General postures not filtered filtered
45. Real video – own sequences General posture recognition rate (%) for the different silhouette representations with “ Watershed algorithm” 93 78 89 100 H. & V. projections 65 82 68 93 Skeletonis-ation 35 27 73 68 Hu moments 83 77 82 94 Geometric features Lying Bending Sitting Standing
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47. Real video – gait sequence 78/81 postures correctly recognised New posture of interest: the walking posture Recognised posture Ground-truth posture Recognised postures 2=standing posture 3=walking posture
48. Real video – gait sequence 162/186 (87%) postures correctly recognised For the 5 sequences: 711/911 (78%) postures are correctly recognised Recognised posture Ground-truth posture Recognised postures 2=standing posture 3=walking posture
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50. Action recognition – the fall Standing 3 ∞ Bending or sitting 0 10 Lying 3 ∞ Based on general postures 0 0 10 Recognised falling action FN FP TP
51. Action recognition – the walk Standing with arms near the body 2 10 Walking 3 15 Based on detailed postures 3 0 62 Recognised walking action FN FP TP
61. Proposed approach Video stream People detection Contextual Knowledge base People tracking Silhouette 3D position Identifier Posture detector Posture filter Recognised posture Behaviour analysis
77. Proposed approach Posture filter Object segmentation Object classification Person tracking People detection Behaviour analysis Detected silhouette Identifier Filtered posture Posture detector Camera parameters
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79. Posture detector – silhouettes generation Camera parameters 3D posture avatars 3D silhouette generator 3D position Virtual camera Generated silhouettes
80. Posture detector – silhouette generation Camera parameters 3D posture avatars 3D silhouette generator 3D position Virtual camera Generated silhouettes
General posture recognition rate authorises that postures belonging to the same general posture can be mixed Detailed posture recognition rate differentiates each detailed postures
We will focus in the next to rotation steps: 36, 45, 90 Representation and comparison times are negligible compared to generation time by considering rotation step superior to 36 degrees. Representation and comparison times are similar for the others repreentations
The GPRR are superior to the DPRR. A rotation step of 36 degrees gives the best recognition rates. The best recognition rates are obtained with the H. & V projections. Hu moments give the worst results. This happens because of the invariance property of this representation. In particular the orientation invariance. For example a standing posture can be mixed with a lying posture.
We are also interested by the problem of intermediate postures which are postures between two postures of interest. We can see on this example the video sequence of a person down her left arm. This video is constituted of two postures of interest: standing with one arm up and standing with arms near the body. We hope to recognise the succession of the three postures: standing, one arm up and standing. The recognition are displaying on the different graphics for each 2D approaches, on the left without temporal filtering and on the right with the temporal filtering. First we can remark that the H.&V. representation recognises correctly the succession of the 3 postures even with no filtering. Second we see that temporal filtering correct wrong recognitions the other representations. Moreover we see that for the Hu moments representation, standing postures is mixed with lying postures.
We have also used synthetic video to identify the ambiguous cases. For example we can see in the table how the T-shape posture is recognised for a given view point.
This graphical interface is composed of 3 parts: The filtered postures can then be used for behaviour analysis.
This table represents the general posture recognition rates for the different 2D approaches according to the watershed algorithm. H & V projections gives the best recognition rates, followed by the geometric features. The recognition is correct with rates superior to 80%. We can notice that Hu moments representation does not work correctly, in particular as seen previously because of the invariance on orientation, and also because when a hole occurs in the silhouette, this error is on all the terms of the Hu moment. Similar results are obtained with the VSIP algorithm. In the next we will focus on H & V projections representation.
We see here the recognition of the detailed postures. Recognition rates are similar for the both segmentations, except for sitting on a chair posture The recognition rates are quite good from 70 up to 80 %
We have also tested our approach on other kind of video sequences. In particular, we are interested in video sequences involved in gait analysis. For this purpose we have introduced a new posture of interest: the walking posture. During the recognition we plan to recognise succession of standing with arms near the body and walking posture. In this video, the silhouettes obtained are good since there is a big contrast between the person and the background. We can see on the graph that the postures are well recognised, and in particular that the gait cycle are well detected
We have also tested our approach on video sequences acquired for the gait competition. In the video the person walk from the right to the left, and the left to the right on a semi ellipse. Even if the silhouettes are noisy, the postures and the gait cycles are well recognised except for a finite cases. On the different videos we have tested … are correctly recognised on … total postures.
Our proposed approach has also been tested for action recognition. We focus on self action i.e. action where only one person is involved.
The first action we have recognised is the fall, which is an important action for medical purpose. For example it can be used for helping elderly person at home. The fall action is characterised by the transition between a standing posture and a lying one. We can see on the video that the falling action is well recognised. Since it is based on genera postures and since these postures are well recognised the action is also well recognised.
The second action we have tested is the walking action. The tests are realised on the sequences taken from the gait challenge competition. The table shows the number of gait cycles correctly recognised. The action is correctly recognised except for a finite number of cases.
In conclusion we can say that the properties highlighting with the synthetical data are verified with the real data. In particular …. The Hu moments are definitely not adapted to or approach. Finally the processing time …
In conclusion, our approach is able to recognise 9 detailed postures which correspond to 4 general postures. The approach have been successfully tested for different type of silhouettes. It has also been tested for self-action recognition. We have identified 4 constraints in the beginning of the introduction. Some work in automated approach and in real time