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.
Vision-based Human Motion Analysis, with Deep Learning
Vision-based Human Motion Analysis, with Deep Learning - 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 Vision-based Human Motion Analysis, with Deep Learning write by Wei Zeng. This book was released on 2019. Vision-based Human Motion Analysis, with Deep Learning available in PDF, EPUB and Kindle.
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 2016. 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.
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.
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.
