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Notice

AISB miscellaneous Bulletin Item

CF Chapter Proposals - Machine Learning for Human Motion Analysis: Theory and Practice

http://www.cs.mu.oz.au/~lwwang/index_files/book_behavior.html

http://www.cs.mu.oz.au/~lwwang/index_files/book_behavior.html

CALL FOR CHAPTER PROPOSALS

Proposal Submission Deadline: July 15, 2008

Machine Learning for Human Motion Analysis: Theory and Practice

A Book Edited by Dr. Liang Wang, The University of Melbourne, Australia

Dr. Li Cheng, National ICT Australia

Dr. Guoying Zhao, University of Oulu, Finland



Introduction
Vision-based motion analysis aims to detect, track and identify objects, 
and more generally, to understand their behaviors, from video sequences. 
With the ubiquitous presence of video data and the increasing importance 
in a wide range of real-world applications such as visual surveillance, 
human-machine interface and sport event interpretation, it is becoming 
increasingly demanding to automatically analyze and understand object 
motions from large amounts of video footage.

Statistical machine learning algorithms have been recently successfully 
applied to address challenging problems involved in this area. Novel 
statistical learning technologies have a strong potential to contribute 
to the development of robust yet flexible vision systems. The process of 
improving the performance of vision systems has also brought new 
challenges to the field of machine learning. Solving the problems 
involved in object motion analysis will lead to the development of new 
machine learning algorithms. In return, new machine learning algorithms 
are able to address more realistic problems in object motion analysis 
and understanding.



Objective of the Book

This edited book will highlight the development of robust and effective 
vision-based motion understanding systems from a machine learning 
perspective. Major contributions of this book are as follows: (1) It 
will provide new researchers with a comprehensive review of the recent 
development in this field, and present a variety of study cases where 
the state-of-the-art learning algorithms are devised to address specific 
tasks in human motion understanding; (2) It will give the readers a 
clear picture of the most active research forefronts and discussions of 
challenges and future directions, which different levels of researchers 
might find to be useful for guiding their future research. (3) It will 
draw great strength from the research communities of human motion 
understanding and machine learning and demonstrates the benefits from 
the interaction and collaboration of both fields.



Target Audience

The targeted audiences are mainly researchers, engineers as well as 
graduate students in the areas of computer vision and machine learning. 
The book is also intend to be accessible to a broader audience including 
practicing professionals working with specific vision applications such 
as surveillance, sport event analysis, healthcare, video conferencing, 
and motion video indexing and retrieval.



Recommended topics include, but are not limited to, the following:

Machine Learning Theories

Supervised/unsupervised/semi-supervised learning
Generative and discriminative approaches
Probabilistic graphical models and exponential families
Large-margin method with structured output
Manifold learning
Kernel machines
Online and incremental learning
Vision-based Motion Analysis and Understanding

Motion segmentation and object recognition
Human detection and tracking
Motion feature extraction and representation
Motion analysis and understanding
Activity analysis and unusual event detection
Machine Learning in Motion Analysis and Understanding

Online and incremental learning to human detection and tracking
Manifold learning for pose estimation
Manifold learning for motion tracking
Large-margin method based action classification
Boosting algorithms for motion feature selection and classification
Generative or discriminative approaches for action modeling
Hybrid graphical models for action recognition
Clustering for categorization of motion patterns
Learning to segment and recognize actions
Learning to discover anomaly activities and events
Learning to analyze motions using stereo and camera array
Learning to analyze activities using multi-modality sensors


Submission Procedure

Researchers and practitioners are invited to submit on or before July 
15, 2008, a 2-3 page chapter proposal clearly explaining the mission and 
concerns of the proposed chapter, together with a tentative title and 
chapter organization. Proposals will be accepted based on pertinence 
criteria and topic balancing needs. Authors of accepted proposals will 
be notified by July 31, 2008 about the status of their proposals and 
sent chapter guidelines. Full chapters are expected to be submitted no 
later than November 30, 2008. All submitted chapters will be reviewed on 
a double-blind review basis. The book is scheduled to be published by 
IGI Global (formerly Idea Group Inc.), www.igi-global.com, publisher of 
the IGI Publishing (formerly Idea Group Publishing), Information Science 
Publishing, IRM Press, CyberTech Publishing, Information Science 
Reference (formerly Idea Group Reference), and Medical Information 
Science Reference imprints.



Inquiries and submissions can be forwarded electronically (Word 
document) or by mail to:



Dr. Liang Wang

Department of Computer Science & Software Engineering

The University of Melbourne, Parkville, Vic 3010, Melbourne, Australia

Tel.: +61 3 8344 1364  Fax: +61 3 9348 1184

Email: lwwang@csse.unimelb.edu.au



Dr. Li Cheng

Canberra Research Lab

National ICT Australia, Locked Bag 8001, Canberra ACT 2612

Mobile: +61 432 572 310

Email: licheng.nicta@gmail.com



Dr. Guoying Zhao
Department of Electrical and Information Engineering
P.O.Box 4500 FI-90014 University of Oulu, Finland
Phone: +358 8 553 7564  Fax: +358 8 553 2612
Email: gyzhao@ee.oulu.fi