A Multi-view Video Based Deep Learning Approach for Human Movement Analysis PDF Download
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Author: Connor McGuirk
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Human motion analysis is an important tool for assessing movement, rehabilitation progress, fall risk, progression of neurodegenerative diseases, and classifying gait patterns. Advancements in artificial intelligence models and high-performance computing technologies have given rise to marker-less human motion analysis that determine point correspondences between an array of cameras and estimate 3D joint coordinates using triangulation. However, existing methods have not considered the physical setup and design of a marker-less human motion analysis tool that could be deployed in an institutional environment for active use, such as an institutional hallway where individuals pass regularly on a daily basis (i.e., Smart Hallway). In this thesis, camera locations were modelled, four machine vision grade cameras connected to an NVIDIA Jetson AGX were set up in a simulated institutional hallway environment, and custom software was written to capture synchronized 60 frame per second video of a participant walking through the Smart Hallway capture volume. Software was also written to calculate 3D joint coordinates and extract outcome measures for various test conditions. These outcome measures were compared to ground truth body segment length measurements obtained from direct measurement and ground truth foot event timings obtained from direct observation. Body segment length measurements were within 1.56 (SD=2.77) cm of ground truth values. AI-based stride parameters were comparable with ground truth foot event timings and the implemented foot event detection algorithm was within 4 frames (67 ms), with an absolute error of 3 frames (50 ms) on the ground truth foot event labels. The Smart Hallway can be deployed in an unobtrusive manner and be temporally and spatially calibrated with ease. This multi-camera marker-less approach is viable for calculating useful outcome measures for clinical decision making. With these findings, marker-less motion capture is viable for non-invasive human motion analysis and compares well with marker-based systems. With future research and innovations, marker-less motion analysis will be the ideal approach for human motion analysis that requires minimal human resource to collect meaningful information.
Author: Connor McGuirk
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Human motion analysis is an important tool for assessing movement, rehabilitation progress, fall risk, progression of neurodegenerative diseases, and classifying gait patterns. Advancements in artificial intelligence models and high-performance computing technologies have given rise to marker-less human motion analysis that determine point correspondences between an array of cameras and estimate 3D joint coordinates using triangulation. However, existing methods have not considered the physical setup and design of a marker-less human motion analysis tool that could be deployed in an institutional environment for active use, such as an institutional hallway where individuals pass regularly on a daily basis (i.e., Smart Hallway). In this thesis, camera locations were modelled, four machine vision grade cameras connected to an NVIDIA Jetson AGX were set up in a simulated institutional hallway environment, and custom software was written to capture synchronized 60 frame per second video of a participant walking through the Smart Hallway capture volume. Software was also written to calculate 3D joint coordinates and extract outcome measures for various test conditions. These outcome measures were compared to ground truth body segment length measurements obtained from direct measurement and ground truth foot event timings obtained from direct observation. Body segment length measurements were within 1.56 (SD=2.77) cm of ground truth values. AI-based stride parameters were comparable with ground truth foot event timings and the implemented foot event detection algorithm was within 4 frames (67 ms), with an absolute error of 3 frames (50 ms) on the ground truth foot event labels. The Smart Hallway can be deployed in an unobtrusive manner and be temporally and spatially calibrated with ease. This multi-camera marker-less approach is viable for calculating useful outcome measures for clinical decision making. With these findings, marker-less motion capture is viable for non-invasive human motion analysis and compares well with marker-based systems. With future research and innovations, marker-less motion analysis will be the ideal approach for human motion analysis that requires minimal human resource to collect meaningful information.
Author: Matteo Zago
Publisher: Frontiers Media SA
ISBN: 2889665615
Category : Science
Languages : en
Pages : 328
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Book Description
Author: Wang, Liang
Publisher: IGI Global
ISBN: 1605669016
Category : Computers
Languages : en
Pages : 318
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Book Description
"This book highlights the development of robust and effective vision-based motion understanding systems, addressing specific vision applications such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval"--Provided by publisher.
Author: Liang Wang
Publisher: Springer Science & Business Media
ISBN: 0857290576
Category : Computers
Languages : en
Pages : 377
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Book Description
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.
Author: Natalia Neverova (informaticienne).)
Publisher:
ISBN:
Category :
Languages : en
Pages : 215
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Book Description
The research goal of this work is to develop learning methods advancing automatic analysis and interpreting of human motion from different perspectives and based on various sources of information, such as images, video, depth, mocap data, audio and inertial sensors. For this purpose, we propose a several deep neural models and associated training algorithms for supervised classification and semi-supervised feature learning, as well as modelling of temporal dependencies, and show their efficiency on a set of fundamental tasks, including detection, classification, parameter estimation and user verification. First, we present a method for human action and gesture spotting and classification based on multi-scale and multi-modal deep learning from visual signals (such as video, depth and mocap data). Key to our technique is a training strategy which exploits, first, careful initialization of individual modalities and, second, gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. Moving forward, from 1 to N mapping to continuous evaluation of gesture parameters, we address the problem of hand pose estimation and present a new method for regression on depth images, based on semi-supervised learning using convolutional deep neural networks, where raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. In separate but related work, we explore convolutional temporal models for human authentication based on their motion patterns. In this project, the data is captured by inertial sensors (such as accelerometers and gyroscopes) built in mobile devices. We propose an optimized shift-invariant dense convolutional mechanism and incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate, that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems.
Author: Natalia Neverova (informaticienne).)
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The research goal of this work is to develop learning methods advancing automatic analysis and interpreting of human motion from different perspectives and based on various sources of information, such as images, video, depth, mocap data, audio and inertial sensors. For this purpose, we propose a several deep neural models and associated training algorithms for supervised classification and semi-supervised feature learning, as well as modelling of temporal dependencies, and show their efficiency on a set of fundamental tasks, including detection, classification, parameter estimation and user verification. First, we present a method for human action and gesture spotting and classification based on multi-scale and multi-modal deep learning from visual signals (such as video, depth and mocap data). Key to our technique is a training strategy which exploits, first, careful initialization of individual modalities and, second, gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. Moving forward, from 1 to N mapping to continuous evaluation of gesture parameters, we address the problem of hand pose estimation and present a new method for regression on depth images, based on semi-supervised learning using convolutional deep neural networks, where raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. In separate but related work, we explore convolutional temporal models for human authentication based on their motion patterns. In this project, the data is captured by inertial sensors (such as accelerometers and gyroscopes) built in mobile devices. We propose an optimized shift-invariant dense convolutional mechanism and incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate, that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems.
Author: Shaogang Gong
Publisher: Springer Science & Business Media
ISBN: 144716296X
Category : Computers
Languages : en
Pages : 446
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Book Description
The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.
Author: Chen Du
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
As video analysis provides an automatic solution to extract meaningful information from the video content, it can be applied in healthcare to evaluate human action patterns for various purposes, such as biometrics estimation and performance assessment. In recent years, the fast development of deep learning and portable medical sensors has led to more affordable and accurate computer vision-based measurements for human action patterns, thus enabling a more efficient video analysis system for action evaluation in home and clinic environments. We investigate the novel usage of video analysis for healthcare monitoring purposes, including objective biometrics estimation and subjective action quality assessment. We propose a deep learning framework to extract spatial-temporal features and estimate biometrics or performance scores from 3D body landmarks using a graph convolutional neural network, which offers a portable solution to obtain gold-standard biometrics with 3D multi-joint coordination underlying body movements and can provide real-time feedback of movement performance for rehabilitation exercises. For biometrics estimation, in Chapter 2, we propose two single-task models for video-level and frame-level estimation, respectively, and a multi-task learning approach to estimate CoP metrics on two different temporal levels in parallel. To facilitate this line of research, we collect and release a novel computer-vision-based 3D body landmark dataset using pose estimation. We extend our framework to a traditional kinematics dataset collected by on-body reflective markers by using adaptive graph convolution. For action quality assessment, we propose a deep learning framework for automatic assessment of physical rehabilitation exercises using a graph convolutional network with self-supervised regularization in Chapter 3. To further improve the accessibility of the real-time CoP metrics estimation system, we investigate a view-invariant video-level CoP metrics estimation framework using a single RGB camera in Chapter 4, which could significantly benefit the data collection in home and clinic environments. We also explore a semi-supervised learning framework for video-level CoP metrics estimation for partially labeled data with only a small portion of labels in Chapter 5. Our proposed methods potentially enable a more affordable, comprehensive, and portable virtual therapy system than is available with existing tools.
Author: John W. Krakauer
Publisher: MIT Press
ISBN: 0262545837
Category : Medical
Languages : en
Pages : 288
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Book Description
An account of the neurobiology of motor recovery in the arm and hand after stroke by two experts in the field. Stroke is a leading cause of disability in adults and recovery is often difficult, with existing rehabilitation therapies largely ineffective. In Broken Movement, John Krakauer and S. Thomas Carmichael, both experts in the field, provide an account of the neurobiology of motor recovery in the arm and hand after stroke. They cover topics that range from behavior to physiology to cellular and molecular biology. Broken Movement is the only accessible single-volume work that covers motor control and motor learning as they apply to stroke recovery and combines them with motor cortical physiology and molecular biology. The authors cast a critical eye at current frameworks and practices, offer new recommendations for promoting recovery, and propose new research directions for the study of brain repair. Krakauer and Carmichael discuss such subjects as the behavioral phenotype of hand and arm paresis in human and non-human primates; the physiology and anatomy of the motor system after stroke; mechanisms of spontaneous recovery; the time course of early recovery; the challenges of chronic stroke; and pharmacological and stem cell therapies. They argue for a new approach in which patients are subjected to higher doses and intensities of rehabilitation in a more dynamic and enriching environment early after stroke. Finally they review the potential of four areas to improve motor recovery: video gaming and virtual reality, invasive brain stimulation, re-opening the sensitive period after stroke, and the application of precision medicine.
Author: Yaxin Bi
Publisher: Springer
ISBN: 3319569945
Category : Technology & Engineering
Languages : en
Pages : 1163
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Book Description
These proceedings of the SAI Intelligent Systems Conference 2016 (IntelliSys 2016) offer a remarkable collection of chapters on a wide range of topics in intelligent systems, artificial intelligence and their applications to the real world. Authors hailing from 56 countries on 5 continents submitted 404 papers to the conference, attesting to the global importance of the conference’s themes. After being reviewed, 222 papers were accepted for presentation, and 168 were ultimately selected for these proceedings. Each has been reviewed on the basis of its originality, novelty and rigorousness. The papers not only present state-of-the-art methods and valuable experience from researchers in the related research areas; they also outline the field’s future development.
