Human Action Detection Tracking And Segmentation In Videos
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Human Action Detection Tracking And Segmentation In Videos
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Author : Yicong Tian
language : en
Publisher:
Release Date : 2018
Human Action Detection Tracking And Segmentation In Videos written by Yicong Tian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This dissertation addresses the problem of human action detection, human tracking and segmentation in videos. They are fundamental tasks in computer vision and are extremely challenging to solve in realistic videos. We first propose a novel approach for action detection by exploring the generalization of deformable part models from 2D images to 3D spatiotemporal volumes. By focusing on the most distinctive parts of each action, our models adapt to intra-class variation and show robustness to clutter. This approach deals with detecting action performed by a single person. When there are multiple humans in the scene, humans need to be segmented and tracked from frame to frame before action recognition can be performed. Next, we propose a novel approach for multiple object tracking (MOT) by formulating detection and data association in one framework. Our method allows us to overcome the confinements of data association based MOT approaches, where the performance is dependent on the object detection results provided at input level. We show that automatically detecting and tracking targets in a single framework can help resolve the ambiguities due to frequent occlusion and heavy articulation of targets. In this tracker, targets are represented by bounding boxes, which is a coarse representation. However, pixel-wise object segmentation provides fine level information, which is desirable for later tasks. Finally, we propose a tracker that simultaneously solves three main problems: detection, data association and segmentation. This is especially important because the output of each of those three problems are highly correlated and the solution of one can greatly help improve the others. The proposed approach achieves more accurate segmentation results and also helps better resolve typical difficulties in multiple target tracking, such as occlusion, ID-switch and track drifting.
Human Detection Tracking And Segmentation In Surveillance Video
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Author : Guang Shu
language : en
Publisher:
Release Date : 2014
Human Detection Tracking And Segmentation In Surveillance Video written by Guang Shu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Compared to previous work, our method could automatically segment multiple people in videos with accurate boundaries, and it is robust to camera motion. Experimental results show that our method achieves better segmentation performance than previous methods in terms of segmentation accuracy on several challenging video sequences. Most of the work in Computer Vision deals with point solution; a specific algorithm for a specific problem. However, putting different algorithms into one real world integrated system is a big challenge. Finally, we introduce an efficient tracking system, NONA, for high-definition surveillance video. We implement the system using a multi-threaded architecture (Intel Threading Building Blocks (TBB)), which executes video ingestion, tracking, and video output in parallel. To improve tracking accuracy without sacrificing efficiency, we employ several useful techniques. Adaptive Template Scaling is used to handle the scale change due to objects moving towards a camera. Incremental Searching and Local Frame Differencing are used to resolve challenging issues such as scale change, occlusion and cluttered backgrounds. We tested our tracking system on a high-definition video dataset and achieved acceptable tracking accuracy while maintaining real-time performance.
Spatio Temporal Human Action Detection And Instance Segmentation In Videos
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Author : Suman Saha
language : en
Publisher:
Release Date : 2018
Spatio Temporal Human Action Detection And Instance Segmentation In Videos written by Suman Saha and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Recognition Of Humans And Their Activities Using Video
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Author : Rama Chellappa
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2006-01-01
Recognition Of Humans And Their Activities Using Video written by Rama Chellappa and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-01 with Technology & Engineering categories.
The recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusion of different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.
Machine Learning For Vision Based Motion Analysis
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Author : Liang Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-18
Machine Learning For Vision Based Motion Analysis written by Liang Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-18 with Computers categories.
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
Intelligent Video Surveillance Systems
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Author : Maheshkumar H Kolekar
language : en
Publisher: CRC Press
Release Date : 2018-06-27
Intelligent Video Surveillance Systems written by Maheshkumar H Kolekar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-27 with Computers categories.
This book will provide an overview of techniques for visual monitoring including video surveillance and human activity understanding. It will present the basic techniques of processing video from static cameras, starting with object detection and tracking. The author will introduce further video analytic modules including face detection, trajectory analysis and object classification. Examining system design and specific problems in visual surveillance, such as the use of multiple cameras and moving cameras, the author will elaborate on privacy issues focusing on approaches where automatic processing can help protect privacy.
Visual Analysis Of Humans
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Author : Thomas B. Moeslund
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-10-08
Visual Analysis Of Humans written by Thomas B. Moeslund and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-08 with Computers categories.
This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Features: with a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includes an extensive glossary; discusses the problems associated with detecting and tracking people through camera networks; examines topics related to determining the time-varying 3D pose of a person from video; investigates the representation and recognition of human and vehicular actions; reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance.
Deep Learning Methods For Video Based Human Activity Recognition In Industrial Settings
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Author : Behnoosh Parsa
language : en
Publisher:
Release Date : 2020
Deep Learning Methods For Video Based Human Activity Recognition In Industrial Settings written by Behnoosh Parsa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
With increasingly high interest in assistive robots and smart surveillance systems, we need a powerful perception mechanism to be able to describe the events in a scene. However, achieving accurate perception models is not trivial, since, even for one perception task there are unlimited possible scenarios. Hoping to develop analytically driven models seems too optimistic for such systems; hence, Supervised Learning as a sub-field of function approximation has become very popular in robotic perception. Supervised learning is the task of learning a function that maps an input to an output based on example input-output pairs. Scene understanding is even more involved when it comes to solving Human Action Recognition (HAR) problems. In HAR the task is to classify human activities from an image or determine atomic actions composing the activity in a video. In video-based HAR, there are exponentially many ways that humans can perform the same task. Besides, the variety in posture and speed at which people perform activities makes solving HAR tasks even more challenging. Therefore, models should be designed to learn common underlying spatial and temporal properties of human activity to achieve generalizability. This thesis is dedicated to designing perception models for recognizing human actions and determining the ergonomic risk associated with them. Specifically, Part I focus on solving the Human Activity Segmentation (HAS) problem in long videos, which is the task of semantically segmenting long videos into distinct actions in an offline framework. In Part II, we present our designs for solving online-HAR problems to recognize human activities in the observed batch of frames. Since, the performance of computer vision algorithms also depends on the quality and relevance of the training data, in Part I, we introduce a new dataset for an indoor object manipulation task called the University of Washington Indoor Object Manipulation (UW-IOM).
Advances In Human Activity Detection And Recognition Hadr Systems
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Author : Santosh Kumar Tripathy
language : en
Publisher: Springer Nature
Release Date :
Advances In Human Activity Detection And Recognition Hadr Systems written by Santosh Kumar Tripathy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Recognizing Human Activity Using Rgbd Data
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Author : Lu Xia
language : en
Publisher:
Release Date : 2014
Recognizing Human Activity Using Rgbd Data written by Lu Xia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Traditional computer vision algorithms try to understand the world using visible light cameras. However, there are inherent limitations of this type of data source. First, visible light images are sensitive to illumination changes and background clutter. Second, the 3D structural information of the scene is lost when projecting the 3D world to 2D images. Recovering the 3D information from 2D images is a challenging problem. Range sensors have existed for over thirty years, which capture 3D characteristics of the scene. However, earlier range sensors were either too expensive, difficult to use in human environments, slow at acquiring data, or provided a poor estimation of distance. Recently, the easy access to the RGBD data at real-time frame rate is leading to a revolution in perception and inspired many new research using RGBD data. I propose algorithms to detect persons and understand the activities using RGBD data. I demonstrate the solutions to many computer vision problems may be improved with the added depth channel. The 3D structural information may give rise to algorithms with real-time and view-invariant properties in a faster and easier fashion. When both data sources are available, the features extracted from the depth channel may be combined with traditional features computed from RGB channels to generate more robust systems with enhanced recognition abilities, which may be able to deal with more challenging scenarios. As a starting point, the first problem is to find the persons of various poses in the scene, including moving or static persons. Localizing humans from RGB images is limited by the lighting conditions and background clutter. Depth image gives alternative ways to find the humans in the scene. In the past, detection of humans from range data is usually achieved by tracking, which does not work for indoor person detection. In this thesis, I propose a model based approach to detect the persons using the structural information embedded in the depth image. I propose a 2D head contour model and a 3D head surface model to look for the head-shoulder part of the person. Then, a segmentation scheme is proposed to segment the full human body from the background and extract the contour. I also give a tracking algorithm based on the detection result. I further research on recognizing human actions and activities. I propose two features for recognizing human activities. The first feature is drawn from the skeletal joint locations estimated from a depth image. It is a compact representation of the human posture called histograms of 3D joint locations (HOJ3D). This representation is view-invariant and the whole algorithm runs at real-time. This feature may benefit many applications to get a fast estimation of the posture and action of the human subject. The second feature is a spatio-temporal feature for depth video, which is called Depth Cuboid Similarity Feature (DCSF). The interest points are extracted using an algorithm that effectively suppresses the noise and finds salient human motions. DCSF is extracted centered on each interest point, which forms the description of the video contents. This descriptor can be used to recognize the activities with no dependence on skeleton information or pre-processing steps such as motion segmentation, tracking, or even image de-noising or hole-filling. It is more flexible and widely applicable to many scenarios. Finally, all the features herein developed are combined to solve a novel problem: first-person human activity recognition using RGBD data. Traditional activity recognition algorithms focus on recognizing activities from a third-person perspective. I propose to recognize activities from a first-person perspective with RGBD data. This task is very novel and extremely challenging due to the large amount of camera motion either due to self exploration or the response of the interaction. I extracted 3D optical flow features as the motion descriptor, 3D skeletal joints features as posture descriptors, spatio-temporal features as local appearance descriptors to describe the first-person videos. To address the ego-motion of the camera, I propose an attention mask to guide the recognition procedures and separate the features on the ego-motion region and independent-motion region. The 3D features are very useful at summarizing the discerning information of the activities. In addition, the combination of the 3D features with existing 2D features brings more robust recognition results and make the algorithm capable of dealing with more challenging cases.