Machine Learning for Human Motion Analysis: Theory and Practice (Premier Reference Source)

With the ubiquitous presence of video data and its increasing importance in a wide range of real-world applications, it is becoming increasingly necessary to automatically analyze and interpret object motions from large quantities of footage. Machine Learning for Human Motion Analysis: Theory and Practice highlights the development of robust and effective vision-based motion understanding systems. This advanced publication addresses a broad audience including practicing professionals working with specific vision applications such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.

Liang Wang obtained the BEng and MEng degrees in electronic engineering from Anhui University and PhD in pattern recognition and intelligent system from National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. From July 2004 to January 2007, he worked at Imperial College London (UK), and at Monash University (Australia), respectively. He is currently working as a research fellow at The University of Melbourne (Australia). His main research interests include pattern recognition, machine learning, computer vision, and data mining. He has widely published at IEEE TPAMI, TIP, TKDE, TCSVT, TSMC, CVIU, PR, CVPR, ICCV, and ICDM. He serves for many major international journals and conferences as AE, reviewer, or PC member. He is currently an associate editor of IEEE TSMC-B, IJIG and Signal Processing. He is a co-editor of four books to be published by IGI Global and Springer, and a guest editor of three special issues for the international journals PRL, IJPRAI and IEEE TSMC-B, as well as co-chairing a special session and three workshops for VM 08, MLVMA 08 and THEMIS 08.

Li Cheng received the BS degree from Jilin University, China, the ME degree from Nankai University, and the PhD degree from the Department of Computing Science, University of Alberta, Canada, in 2004. He worked as a research associate in the same department at the University of Alberta, and now he is with the Machine Learning group, NICTA Australia, and TTI-Chicago USA as a PostDoc. He has published about 25 research papers. Together with A. Smola and M. Hutter, he co-organized a machine learning summer school. His research interests are mainly on image and video understanding, computer vision and machine learning.

Guoying Zhao received the PhD degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China in 2005. Since July 2005, she has been a Postdoctoral Research Fellow in Machine Vision Group at the University of Oulu. Her research interests include gait analysis, dynamic texture recognition, facial expression recognition, human motion analysis, and person identification. She has authored over 50 papers in journals and conferences, and has served as a reviewer for many journals and conferences. She gave an invited talk Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions in Institute of Computing Technology, Chinese Academy of Sciences, July 2007. With Prof. Pietikäinen, she gave a tutorial: Local Binary Pattern Approach to Computer Vision in 18th ICPR, Aug. 2006, Hong Kong. She is authoring/editing three books to be published (IGI or Springer). She is guest editor of the special issue New Advances in Video-based Gait Analysis and Applications: Challenges and Solutions on IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics. She was a co-chair of ECCV 2008 Workshop on Machine Learning for Vision-based Motion Analysis (MLVMA), and is a co-chair of MLVMA workshop at ICCV 2009.