A Multi-view Video Based Deep Learning Approach for Human Movement Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Multi-view Video Based Deep Learning Approach for Human Movement Analysis write by Connor McGuirk. This book was released on 2021. A Multi-view Video Based Deep Learning Approach for Human Movement Analysis available in PDF, EPUB and Kindle. 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.
Machine Learning Approaches to Human Movement Analysis
Machine Learning Approaches to Human Movement Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning Approaches to Human Movement Analysis write by Matteo Zago. This book was released on 2021-03-04. Machine Learning Approaches to Human Movement Analysis available in PDF, EPUB and Kindle.
Machine Learning for Human Motion Analysis: Theory and Practice
Machine Learning for Human Motion Analysis: Theory and Practice - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for Human Motion Analysis: Theory and Practice write by Wang, Liang. This book was released on 2009-12-31. Machine Learning for Human Motion Analysis: Theory and Practice available in PDF, EPUB and Kindle. "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.
Machine Learning for Vision-Based Motion Analysis
Machine Learning for Vision-Based Motion Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for Vision-Based Motion Analysis write by Liang Wang. This book was released on 2010-11-18. Machine Learning for Vision-Based Motion Analysis available in PDF, EPUB and Kindle. 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.
Deep Learning for Human Motion Analysis
Deep Learning for Human Motion Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning for Human Motion Analysis write by Natalia Neverova (informaticienne).). This book was released on 2020. Deep Learning for Human Motion Analysis available in PDF, EPUB and Kindle. 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.
