A Novel Occlusion-Adaptive Object Tracking Method
In conventional object tracking methods, much attention has been paid to tracking efficiency, but they often failed in tracking an occluded object. In this paper, we present a new method to improve the occlusion adaptability and tracking robustness. This proposed algorithm covers the occlusion-adaptive particle filter (OAPF) framework, which employs the adaptive state transition model to detect occlusions by a first-order histogram difference dynamic algorithm accurately and simply. Thus, when partial or complete occlusions occur, it can detect interrupted state transition to realize persistent tracking. In addition, tracking robustness is also upgraded via adaptive Gaussian noise coefficient model in particle propagation. Finally, we emphasize that the computing complexity of OAPF is evidently decreased by reducing the particle number in execution. As a result, this simple and effective occlusion-adaptive tracking method has been demonstrated through several real-time sequences.
Object Tracking Particle Filter Occlusion Adaptation Adaptive Soise Model
Xiaofeng Lu Li Song Yi Xu Songyu Yu
Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai, 2000240, China Shanghai Key Labs of Digital Media Processing and Communication Shanghai Jiao Tong University Shanghai, 2000240, China
国际会议
太原
英文
408-412
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）