Efficient multiple faces tracking based on Relevance Vector Machine and Boosting learning |
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| Affiliation: | 1. Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;2. Institute of Digital Media, Peking University, Beijing 100871, China;1. The State Key Laboratory of Astronautic Dynamics, Xian 710038, China;2. Center for Radio Administration & Technology Development, Xihua University, Chengdu 610039, China;1. Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China;2. College of Mathematics, Physics, and Information Engineering, Zhejiang Normal University, Jinhua 321004, China;3. Laboratory of Intelligent Control and Robotics, Shanghai University of Engineering Science, Shanghai 201620, China;4. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;1. Sorbonne Universités, UPMC Université Paris 06, CNRS, INRIA, LIP6 UMR 7606, Paris, France;2. MIS Lab., Université de Picardie Jules Verne, France;3. Département d''informatique, Université du Québec en Outaouais, Gatineau, Québec J8X 3X7, Canada |
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| Abstract: | A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process different tracking methods (RVM tracking, local search, giving up tracking) are used according to different states of faces, and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces. In the full image search and local search, the similarity matrix is introduced to help matching faces efficiently. Experimental results demonstrate that this system can (a) automatically find new coming faces; (b) recover from occlusion, for example, if the faces are occluded by others and reappear or leave the scene and return; (c) run with a high computation efficiency, run at about 20 frames/s. |
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