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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.
"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.
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.
Cameras for 3D depth imaging, using either time-of-flight (ToF) or structured light sensors, have received a lot of attention recently and have been improved considerably over the last few years. The present techniques make full-range 3D data available at video frame rates, and thus pave the way for a much broader application of 3D vision systems. A series of workshops have closely followed the developments within ToF imaging over the years. Today, depth imaging workshops can be found at every major computer vision conference. The papers presented in this volume stem from a seminar on Time-of-Flight Imaging held at Schloss Dagstuhl in October 2012. They cover all aspects of ToF depth imaging, from sensors and basic foundations, to algorithms for low level processing, to important applications that exploit depth imaging. In addition, this book contains the proceedings of a workshop on Imaging New Modalities, which was held at the German Conference on Pattern Recognition in Saarbrücken, Germany, in September 2013. A state-of-the-art report on the Kinect sensor and its applications is followed by two reports on local and global ToF motion compensation and a novel depth capture system using a plenoptic multi-lens multi-focus camera sensor.
This book discusses the concepts, theory, and core technologies of intelligent theory and human animation, including video based human animation and intelligent technology of motion data management and reusing. It introduces systems developed to demonstrate the technologies of video based animation. Lively pictures and demos throughout the text help make the theory and technologies more accessible to readers.
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.
This book constitutes the refereed proceedings of the 8th International Conference on Articulated Motion and Deformable Objects, AMDO 2014, held in Palma de Mallorca, Spain, in July 2014. The 18 papers presented were carefully reviewed and selected from 37 submissions. The conference dealt with the following topics: geometric and physical deformable models; motion analysis; articulated models and animation; modeling and visualization of deformable models; deformable model applications; motion analysis applications; single or multiple human motion analysis and synthesis; face modeling, tracking, recovering and recognition models; virtual and augmented reality; haptics devices; biometric techniques.
Motion-based recognition deals with the recognition of an object and/or its motion, based on motion in a series of images. In this approach, a sequence containing a large number of frames is used to extract motion information. The advantage is that a longer sequence leads to recognition of higher level motions, like walking or running, which consist of a complex and coordinated series of events. Unlike much previous research in motion, this approach does not require explicit reconstruction of shape from the images prior to recognition. This book provides the state-of-the-art in this rapidly developing discipline. It consists of a collection of invited chapters by leading researchers in the world covering various aspects of motion-based recognition including lipreading, gesture recognition, facial expression recognition, gait analysis, cyclic motion detection, and activity recognition. Audience: This volume will be of interest to researchers and post- graduate students whose work involves computer vision, robotics and image processing.
This book constitutes the refereed proceedings of the Second Workshop on Human Motion, HumanMotion 2007, held in Rio de Janeiro, Brazil October 2007 in conjunction with ICCV 2007. The 22 revised full papers presented were carefully reviewed and selected from 38 submissions. The papers are organized in topical sections on motion capture and pose estimation, body and limb tracking and segmentation and activity recognition.