title: Part-based visual tracking via online weighted P-N learning.
abstract: We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.
authors.affiliation: College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.
authors.forename: Heng
authors.initials: H
authors.lastname: Fan
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan 430070, China.
authors.forename: Jinhai
authors.initials: J
authors.lastname: Xiang
authors.affiliation: Department of Physics, Central China Normal University, Wuhan 430079, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Xu
authors.affiliation: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
authors.forename: Honghong
authors.identifier: 0000-0002-4404-940X
authors.initials: H
authors.lastname: Liao
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D061848:Optical Imaging / Q000379:methods*; D058998:Remote Sensing Technology / Q000379:methods*