An Approach to Overcome Occlusions in Visual Tracking: By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training

doi:10.1109/LSP.2018.2856102

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	An Approach to Overcome Occlusions in Visual Tracking: By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training
	Zhang, Bao 2; Song, Ce 2; Qian, Feng 2; Jiang, Kaiwen 1,2
刊名	IEEE SIGNAL PROCESSING LETTERS
	2018-12-01
卷号	25 期号:12 页码:1890-1894
关键词	Overcome occlusion regional color histogram (RCH) self-adopting learning rate visual tracking
ISSN号	1070-9908
DOI	10.1109/LSP.2018.2856102
通讯作者	Zhang, Bao(zhangb@ciomp.ac.cn)
英文摘要	Visual tracking methods have been successful in recent years. Correlation filter (CF) based methods significantly advanced state-of-the-art tracking. The advancement in CF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, there would be trouble if the tracker indiscriminately learned samples. Particularly, when the target is severely occluded or out-of-view, the tracker will continuously learn the wrong information, resulting target loss in the following frames. In this study, aiming to avoid incorrect training when occlusions occur, we propose a regional color histogram-based occlusion estimating agency (RCHBOEA), which estimates the occlusion level and then instructs, based on the result, the tracker to work in one of two modes: normal or lost. In the normal mode, an occlusion level-based self-adopting learning rate is used for tracker training. In the lost mode, the tracker pauses its training and conducts a search and recapture strategy on a wider searching area. Our method can easily complement CF-based trackers. In our experiments, we employed four CF-based trackers as a baseline: discriminative CFs (DCF), kernelized CFs (KCF), background-aware CFs (BACF), and efficient convolution operators for tracking: hand-crafted feature version (ECO_HC). We performed extensive experiments on the standard benchmarks: VIVID, OTB50, and OTB100. The results demonstrated that combined with RCHBOEA, the trackers achieved a remarkable improvement.
资助项目	National Natural Science Foundation of China[61705225]
WOS关键词	OBJECT TRACKING ; BENCHMARK
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000449973100008
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/60289]
专题	中国科学院长春光学精密机械与物理研究所
通讯作者	Zhang, Bao
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Bao,Song, Ce,Qian, Feng,et al. An Approach to Overcome Occlusions in Visual Tracking: By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training[J]. IEEE SIGNAL PROCESSING LETTERS,2018,25(12):1890-1894.
APA	Zhang, Bao,Song, Ce,Qian, Feng,&Jiang, Kaiwen.(2018).An Approach to Overcome Occlusions in Visual Tracking: By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training.IEEE SIGNAL PROCESSING LETTERS,25(12),1890-1894.
MLA	Zhang, Bao,et al."An Approach to Overcome Occlusions in Visual Tracking: By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training".IEEE SIGNAL PROCESSING LETTERS 25.12(2018):1890-1894.