Machine Learning for Vision-Based Motion Analysis: Theory and Techniques

by Liang Wang (Editor), Guoying Zhao (Editor), Li Cheng (Editor), Matti Pietikäinen (Editor)

Overview

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.

Product Details

ISBN-13:	9780857290564
Publisher:	Springer London
Publication date:	11/19/2010
Series:	Advances in Computer Vision and Pattern Recognition
Edition description:	2011
Pages:	372
Product dimensions:	6.20(w) x 9.20(h) x 1.20(d)

Table of Contents

Part I Manifold Learning and Clustering/Segmentation

Practical Algorithms of Spectral Clustering: Toward Large-Scale Vision-Based Motion Analysis Tomoya Sakai Atsushi Imiya 3

Riemannian Manifold Clustering and Dimensionality Reduction for Vision-Based Analysis Alvina Goh 27

Manifold Learning for Multi-dimensional Auto-regressive Dynamical Models Fabio Cuzzolin 55

Part II Tracking

Mixed-State Markov Models in Image Motion Analysis Tomás Crivelli Patrick Bouthemy Bruno Cernuschi Frías Jian-feng Yao 77

Learning to Detect Event Sequences in Surveillance Streams at Very Low Frame Rate Paolo Lombardi Cristina Versino 117

Discriminative Multiple Target Tracking Xiaoyu Wang Gang Hua Tony X. Han 145

A Framework of Wire Tracking in Image Guided Interventions Peng Wang Andreas Meyer Terrence Chen Shaohua K. Zhou Dorin Comaniciu 159

Part III Motion Analysis and Behavior Modeling

An Integrated Approach to Visual Attention Modeling for Saliency Detection in Videos Sunaad Nataraju Vineeth Balasubramanian Sethuraman Panchanathan 181

Video-Based Human Motion Estimation by Part-Whole Gait Manifold Learning Guoliang Fan Xin Zhang 215

Spatio-Temporal Motion Pattern Models of Extremely Crowded Scenes Louis Kratz Ko Nishino 263

Learning Behavioral Patterns of Time Series for Video-Surveillance Nicoletta Noceti Matteo Santoro Francesca Odone 275

Part IV Gesture and Action Recognition

Recognition of Spatiotemporal Gestures in Sign Language Using Gesture Threshold HMMs Daniel Kelly John McDonald Charles Markham 307

Learning Transferable Distance Functions for Human Action Recognition Weilong Yang Yang Wang Greg Mori 349

Index 371

Editorial 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)

From the Publisher