Advances in Computer Vision and Pattern Recognition

Machine Learning for Vision-Based Motion Analysis

Theory and Techniques

Editors: Wang, L., Zhao, G., Cheng, L., Pietikäinen, M. (Eds.)

  • Provides a comprehensive and accessible review of vision-based motion analysis
  • Highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective
  • Describes the benefits of collaboration between the communities of object motion understanding and machine learning
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Hardcover $189.00
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  • ISBN 978-0-85729-056-4
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Softcover $189.00
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  • ISBN 978-1-4471-2607-2
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About this book

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.

Dr. Liang Wang is a lecturer at the Department of Computer Science at the University of Bath, UK, and is also affiliated to the National Laboratory of Pattern Recognition in Beijing, China. Dr. Guoying Zhao is an adjunct professor at the Department of Electrical and Information Engineering at the University of Oulu, Finland. Dr. Li Cheng is a research scientist at the Agency for Science, Technology and Research (A*STAR), Singapore. Dr. Matti Pietikäinen is Professor of Information Technology at the Department of Electrical and Information Engineering at the University of Oulu, Finland.

Reviews

From the reviews:

“The successes of the First and Second International Workshops on Machine Learning for Vision-Based Motion Analysis, which were held in 2008 and 2009, prompted this book. The book consists of four parts, and each part includes a number of freestanding chapters. … This book provides a comprehensive introduction to machine learning for vision-based motion analysis. I would recommend it to students and researchers who are interested in learning about the topic.” (J. P. E. Hodgson, ACM Computing Reviews, June, 2011)


Table of contents (13 chapters)

  • Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis

    Sakai, Tomoya (et al.)

    Pages 3-26

  • Riemannian Manifold Clustering and Dimensionality Reduction for Vision-Based Analysis

    Goh, Alvina

    Pages 27-53

  • Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models

    Cuzzolin, Fabio

    Pages 55-74

  • Mixed-State Markov Models in Image Motion Analysis

    Crivelli, Tomás (et al.)

    Pages 77-115

  • Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate

    Lombardi, Paolo (et al.)

    Pages 117-144

Buy this book

eBook $149.00
price for USA
  • ISBN 978-0-85729-057-1
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Download immediately after purchase
Hardcover $189.00
price for USA
  • ISBN 978-0-85729-056-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $189.00
price for USA
  • ISBN 978-1-4471-2607-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning for Vision-Based Motion Analysis
Book Subtitle
Theory and Techniques
Editors
  • Liang Wang
  • Guoying Zhao
  • Li Cheng
  • Matti Pietikäinen
Series Title
Advances in Computer Vision and Pattern Recognition
Copyright
2011
Publisher
Springer-Verlag London
Copyright Holder
Springer-Verlag London Limited
eBook ISBN
978-0-85729-057-1
DOI
10.1007/978-0-85729-057-1
Hardcover ISBN
978-0-85729-056-4
Softcover ISBN
978-1-4471-2607-2
Series ISSN
2191-6586
Edition Number
1
Number of Pages
XIV, 372
Topics