CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Robust Online Learned Spatio-Temporal Context Model for Visual Tracking
Wen, Longyin1,2; Cai, Zhaowei1,2; Lei, Zhen1,2; Yi, Dong1,2; Li, Stan Z.1,2
AbstractVisual 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.
KeywordVisual Tracking Spatio-temporal Context Multiple Subspaces Learning Online Boosting
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000329581800023
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Cited Times:32[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
Recommended Citation
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.
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