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Báo cáo hóa học: " Research Article Using Gaussian Process Annealing Particle Filter for 3D Human Tracking"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Using Gaussian Process Annealing Particle Filter for 3D Human Tracking | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 592081 13 pages doi 10.1155 2008 592081 Research Article Using Gaussian Process Annealing Particle Filter for 3D Human Tracking Leonid Raskin Ehud Rivlin and Michael Rudzsky Computer Science Department Technion - Israel Institute of Technology Technion City Haifa 32000 Israel Correspondence should be addressed to Leonid Raskin raskinl@cs.technion.ac.il Received 31 January 2007 Revised 14 June 2007 Accepted 16 September 2007 Recommended by Enis Ahmet Cetin We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian process annealing particle filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker s stability and robustness. Comparing with a regular annealed particle filter-based tracker we show that our algorithm can track better for low frame rate videos. We also show that our algorithm is capable of recovering after a temporal target loss. Copyright 2008 Leonid Raskin et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Human body pose estimation and tracking is a challenging task for several reasons. First the large dimensionality of the human 3D model complicates the examination of the entire subject and makes it harder to detect each body part separately. Secondly the significantly different appearance of different people that stems from various clothing styles and illumination variations adds to the already great variety of images of different individuals. Finally the most challenging difficulty that has to be solved in order to achieve satisfactory results of pose understanding is the ambiguity caused bybody. This paper presents an approach to 3D articulated human