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Robust Online Learned Spatio-Temporal Context Model for Visual Tracking

文献类型：期刊论文

作者Wen, Longyin1,2; Cai, Zhaowei1,2; Lei, Zhen1,2; Yi, Dong1,2; Li, Stan Z.1,2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2014-02-01
卷号23期号:2页码:785-796
关键词Visual tracking spatio-temporal context multiple subspaces learning online boosting
英文摘要Visual tracking is an important but challenging problem in the computer vision field. In the real world, the appearances of the target and its surroundings change continuously over space and time, which provides effective information to track the target robustly. However, enough attention has not been paid to the spatio-temporal appearance information in previous works. In this paper, a robust spatio-temporal context model based tracker is presented to complete the tracking task in unconstrained environments. The tracker is constructed with temporal and spatial appearance context models. The temporal appearance context model captures the historical appearance of the target to prevent the tracker from drifting to the background in a long-term tracking. The spatial appearance context model integrates contributors to build a supporting field. The contributors are the patches with the same size of the target at the key-points automatically discovered around the target. The constructed supporting field provides much more information than the appearance of the target itself, and thus, ensures the robustness of the tracker in complex environments. Extensive experiments on various challenging databases validate the superiority of our tracker over other state-of-the-art trackers.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]OBJECTS
收录类别SCI
语种英语
WOS记录号WOS:000329581800023
公开日期2015-09-22
源URL[http://ir.ia.ac.cn/handle/173211/8032]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
作者单位1.Chinese Acad Sci, Inst Automat, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wen, Longyin,Cai, Zhaowei,Lei, Zhen,et al. Robust Online Learned Spatio-Temporal Context Model for Visual Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(2):785-796.
APA Wen, Longyin,Cai, Zhaowei,Lei, Zhen,Yi, Dong,&Li, Stan Z..(2014).Robust Online Learned Spatio-Temporal Context Model for Visual Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(2),785-796.
MLA Wen, Longyin,et al."Robust Online Learned Spatio-Temporal Context Model for Visual Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.2(2014):785-796.

入库方式： OAI收割

来源：自动化研究所

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