Download or read book Machine Learning for Human Motion Analysis Theory and Practice written by Wang, Liang and published by IGI Global. This book was released on 2009-12-31 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.
Download or read book Machine Learning Approaches to Human Movement Analysis written by Matteo Zago and published by Frontiers Media SA. This book was released on 2021-03-04 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning for Human Motion Analysis and Gesture Recognition written by Loren Arthur Schwarz and published by . This book was released on 2012 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Deep Learning for Human Motion Analysis written by Natalia Neverova (informaticienne).) and published by . This book was released on 2020 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Deep Learning for Human Motion Analysis written by Natalia Neverova (informaticienne).) and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning for Vision Based Motion Analysis written by Liang Wang and published by Springer Science & Business Media. This book was released on 2010-11-18 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Vision based Human Motion Analysis with Deep Learning written by Wei Zeng and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Human Motion Sensing and Recognition written by Honghai Liu and published by Springer. This book was released on 2017-05-11 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the latest exciting advances in human motion sensing and recognition, from the theoretical development of fuzzy approaches to their applications. The topics covered include human motion recognition in 2D and 3D, hand motion analysis with contact sensors, and vision-based view-invariant motion recognition, especially from the perspective of Fuzzy Qualitative techniques. With the rapid development of technologies in microelectronics, computers, networks, and robotics over the last decade, increasing attention has been focused on human motion sensing and recognition in many emerging and active disciplines where human motions need to be automatically tracked, analyzed or understood, such as smart surveillance, intelligent human-computer interaction, robot motion learning, and interactive gaming. Current challenges mainly stem from the dynamic environment, data multi-modality, uncertain sensory information, and real-time issues. These techniques are shown to effectively address the above challenges by bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in intelligent systems. The book not only serves as a valuable reference source for researchers and professionals in the fields of computer vision and robotics, but will also benefit practitioners and graduates/postgraduates seeking advanced information on fuzzy techniques and their applications in motion analysis.
Download or read book A Multi view Video Based Deep Learning Approach for Human Movement Analysis written by Connor McGuirk and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Social Robotics written by Shuzhi Sam Ge and published by Springer. This book was released on 2012-11-04 with total page 679 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Conference on Social Robotics, ICSR 2012, held in Chengdu, China, in October 2012. The 66 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on affective and cognitive sciences for socially interactive robots, situated interaction and embodiment, robots to assist the elderly and persons with disabilities, social acceptance of robots and their impact to the society, artificial empathy, HRI through non-verbal communication and control, social telepresence robots, embodiments and networks, interaction and collaboration among robots, humans and environment, human augmentation, rehabilitation, and medical robots I and II.
Download or read book Modelling Human Motion written by Nicoletta Noceti and published by Springer Nature. This book was released on 2020-07-09 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new frontiers of robotics research foresee future scenarios where artificial agents will leave the laboratory to progressively take part in the activities of our daily life. This will require robots to have very sophisticated perceptual and action skills in many intelligence-demanding applications, with particular reference to the ability to seamlessly interact with humans. It will be crucial for the next generation of robots to understand their human partners and at the same time to be intuitively understood by them. In this context, a deep understanding of human motion is essential for robotics applications, where the ability to detect, represent and recognize human dynamics and the capability for generating appropriate movements in response sets the scene for higher-level tasks. This book provides a comprehensive overview of this challenging research field, closing the loop between perception and action, and between human-studies and robotics. The book is organized in three main parts. The first part focuses on human motion perception, with contributions analyzing the neural substrates of human action understanding, how perception is influenced by motor control, and how it develops over time and is exploited in social contexts. The second part considers motion perception from the computational perspective, providing perspectives on cutting-edge solutions available from the Computer Vision and Machine Learning research fields, addressing higher-level perceptual tasks. Finally, the third part takes into account the implications for robotics, with chapters on how motor control is achieved in the latest generation of artificial agents and how such technologies have been exploited to favor human-robot interaction. This book considers the complete human-robot cycle, from an examination of how humans perceive motion and act in the world, to models for motion perception and control in artificial agents. In this respect, the book will provide insights into the perception and action loop in humans and machines, joining together aspects that are often addressed in independent investigations. As a consequence, this book positions itself in a field at the intersection of such different disciplines as Robotics, Neuroscience, Cognitive Science, Psychology, Computer Vision, and Machine Learning. By bridging these different research domains, the book offers a common reference point for researchers interested in human motion for different applications and from different standpoints, spanning Neuroscience, Human Motor Control, Robotics, Human-Robot Interaction, Computer Vision and Machine Learning. Chapter 'The Importance of the Affective Component of Movement in Action Understanding' of this book is available open access under a CC BY 4.0 license at link.springer.com.
Download or read book Patterns of Human Motion written by Stanley Plagenhoef and published by Prentice Hall. This book was released on 1971 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-09 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
Download or read book Machine Learning Applied to Human Motion and Behavior Analysis written by Richard J. Parker and published by . This book was released on 2004 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Human Motion written by and published by Springer. This book was released on 2018-04-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Human Motion is a large cross-disciplinary reference work which covers the many interlinked facets of the science and technology of human motion and its measurement. Individual chapters cover fundamental principles and technological developments, the state-of-the-art and consider applications across four broad and interconnected fields; medicine, sport, forensics and animation. The huge strides in technological advancement made over the past century make it possible to measure motion with unprecedented precision, but also lead to new challenges. This work introduces the many different approaches and systems used in motion capture, including IR and ultrasound, mechanical systems and video, plus some emerging techniques. The large variety of techniques used for the study of motion science in medicine can make analysis a complicated process, but extremely effective for the treatment of the patient when well utilised. The handbook descri bes how motion capture techniques are applied in medicine, and shows how the resulting analysis can help in diagnosis and treatment. A closely related field, sports science involves a combination of in-depth medical knowledge and detailed understanding of performance and training techniques, and motion capture can play an extremely important role in linking these disciplines. The handbook considers which technologies are most appropriate in specific circumstances, how they are applied and how this can help prevent injury and improve sporting performance. The application of motion capture in forensic science and security is reviewed, with chapters dedicated to specific areas including employment law, injury analysis, criminal activity and motion/facial recognition. And in the final area of application, the book describes how novel motion capture techniques have been designed specifically to aid the creation of increasingly realistic animation within films and v ideo games, with Lord of the Rings and Avatar just two examples. Chapters will provide an overview of the bespoke motion capture techniques developed for animation, how these have influenced advances in film and game design, and the links to behavioural studies, both in humans and in robotics. Comprising a cross-referenced compendium of different techniques and applications across a broad field, the Handbook of Human Motion provides the reader with a detailed reference and simultaneously a source of inspiration for future work. The book will be of use to students, researchers, engineers and others working in any field relevant to human motion capture.
Download or read book Computational Intelligence for Movement Sciences Neural Networks and Other Emerging Techniques written by Begg, Rezaul and published by IGI Global. This book was released on 2006-02-28 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides information regarding state-of-the-art research outcomes and cutting-edge technology on various aspects of the human movement"--Provided by publisher.
Download or read book Human Motion written by Bodo Rosenhahn and published by Springer Science & Business Media. This book was released on 2008 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book which informs about recent progress in biomechanics, computer vision and computer graphics – all in one volume. Researchers from these areas have contributed to this book to promote the establishment of human motion research as a multi-facetted discipline and to improve the exchange of ideas and concepts between these three areas. The book combines carefully written reviews with detailed reports on recent progress in research.
