© 2011

Machine Learning for Vision-Based Motion Analysis

Theory and Techniques

  • Liang Wang
  • Guoying Zhao
  • Li Cheng
  • Matti Pietikäinen

Part of the Advances in Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Manifold Learning and Clustering/Segmentation

  3. Tracking

    1. Front Matter
      Pages 75-75
    2. Tomás Crivelli, Patrick Bouthemy, Bruno Cernuschi Frías, Jian-feng Yao
      Pages 77-115
    3. Xiaoyu Wang, Gang Hua, Tony X. Han
      Pages 145-158
    4. Peng Wang, Andreas Meyer, Terrence Chen, Shaohua K. Zhou, Dorin Comaniciu
      Pages 159-177
  4. Motion Analysis and Behavior Modeling

    1. Front Matter
      Pages 179-179
    2. Sunaad Nataraju, Vineeth Balasubramanian, Sethuraman Panchanathan
      Pages 181-214
    3. Nicoletta Noceti, Matteo Santoro, Francesca Odone
      Pages 275-304
  5. Gesture and Action Recognition

    1. Front Matter
      Pages 305-305
    2. Daniel Kelly, John McDonald, Charles Markham
      Pages 307-348
    3. Weilong Yang, Yang Wang, Greg Mori
      Pages 349-370
  6. Back Matter
    Pages 371-372

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.


Computer Vision Graphical Models Kernel Machines Machine Learning Manifold Learning Motion Analysis Visual Event Analysis

Editors and affiliations

  • Liang Wang
    • 1
  • Guoying Zhao
    • 2
  • Li Cheng
    • 3
  • Matti Pietikäinen
    • 4
  1. 1.Department of Computer ScienceUniversity of BathBathUnited Kingdom
  2. 2.Dept. Electrical and Information Eng.University of OuluOuluFinland
  3. 3.Bioinformatics InstituteA*STARSingaporeSingapore
  4. 4.Department of Electrical Engineering, Machine Vision & Media Processing UnitUniversity of OuluOuluFinland

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 Pattern Recognition
  • Series Abbreviated Title Advances in Pattern Recognition
  • DOI
  • Copyright Information Springer-Verlag London Limited 2011
  • Publisher Name Springer, London
  • eBook Packages Computer Science Computer Science (R0)
  • Hardcover ISBN 978-0-85729-056-4
  • Softcover ISBN 978-1-4471-2607-2
  • eBook ISBN 978-0-85729-057-1
  • Series ISSN 2191-6586
  • Series E-ISSN 2191-6594
  • Edition Number 1
  • Number of Pages XIV, 372
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Image Processing and Computer Vision
    Artificial Intelligence
  • Buy this book on publisher's site
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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)