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《Electronics Optics & Control》 2017-01
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Superpixel Tracking via Online Multiple Instance Learning

WANG Wei;WANG Chun-ping;FU Qiang;XU Yan;OU Xin-yu;Ordnance Engineering College;Department of Computer Science and Technology, Tsinghua University;Yunnan Province Cadres Online Learning College, Yunnan Open University;  
Conventional tracking methods describe the target with a bounding box. As the bounding box is likely to contain some background regions and will degrade the tracking performance, a superpixel tracking method via online multiple instance learning is proposed. In training stage, input frame is segmented into superpixels, which are divided into several instance bags with clear labels according to their location. The tracking is thus converted into a multiple instance learning problem. Then, online multiple instance learning is implemented with the algorithm. The maximum of instance bags' log-likelihood function is calculated to get K best weak classifiers, which are combined into a strong classifier. In detection stage, a confidence map is generated by the strong classifier in the subsequent frame. Finally, the state of the tracking target is estimated with the confidence map in particle filter framework. The proposed method runs at a rate of 15 frames per second on a laptop. Extensive experimental results on challenging sequences show that the proposed method performs well in terms of robustness and accuracy, especially for the target under complex background, moving at high-speed or is occluded. Compared with the original superpixel tracking, the typical values of precision and success rate of the proposed method are increased by 21% and 26%, reaching 91% and 90%, respectively.
【Fund】: 国家自然科学基金(61141009)
【CateGory Index】: TP391.41
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【Citations】
Chinese Journal Full-text Database 1 Hits
1 CHEN Dong-cheng;ZHU Ming;GAO Wen;SUN Hong-hai;YANG Wen-bo;Key Laboratory of Airborne Optical Imaging and Measurement,Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences;Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences;University of Chinese Academy of Sciences;;Real-time object tracking via online weighted multiple instance learning[J];光学精密工程;2014-06
【Co-citations】
Chinese Journal Full-text Database 10 Hits
1 CAI Hua;CHEN Guang-qiu;LIU Guang-wen;CHENG Shuai;YU Hua-dong;School of Electronic and Information Engineering,Changchun University of Science and Technology;College of Mechanical and Electric Engineering,Changchun University of Science and Technology;;Novelty fragments-based target tracking with multiple instance learning under occlusions[J];吉林大学学报(工学版);2017-01
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3 WANG Wei;WANG Chun-ping;FU Qiang;XU Yan;OU Xin-yu;Ordnance Engineering College;Department of Computer Science and Technology, Tsinghua University;Yunnan Province Cadres Online Learning College, Yunnan Open University;;Superpixel Tracking via Online Multiple Instance Learning[J];电光与控制;2017-01
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