CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Max-Confidence Boosting With Uncertainty for Visual Tracking
Guo, Wen1,2; Cao, Liangliang3; Han, Tony X.4; Yan, Shuicheng5; Xu, Changsheng2
AbstractThe challenges in visual tracking call for a method which can reliably recognize the subject of interests in an environment, where the appearance of both the background and the foreground change with time. Many existing studies model this problem as tracking by classification with online updating of the classification models, however, most of them overlook the ambiguity in visual modeling and do not consider the prior information in the tracking process. In this paper, we present a novel visual tracking method called max-confidence boosting (MCB), which explores a new way of online updating ambiguous visual phenomenon. The MCB framework models uncertainty in prior knowledge utilizing the indeterministic labels, which are used in updating models from previous frames and the new frame. Our proposed MCB tracker allows ambiguity in the tracking process and can effectively alleviate the drift problem. Many experimental results in challenging video sequences verify the success of our method, and our MCB tracker outperforms a number of the state-of-the-art tracking by classification methods.
KeywordMax-confidence Boosting Semi-supervised Learning Visual Tracking
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000352087100004
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Document Type期刊论文
Affiliation1.Shandong Business & Technol Univ, Dept Elect Engn, Yantai 264003, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.IBM Watson Res Ctr, New York, NY 10598 USA
4.Univ Missouri, Columbia, MO 65211 USA
5.Natl Univ Singapore, Singapore 119077, Singapore
Recommended Citation
GB/T 7714
Guo, Wen,Cao, Liangliang,Han, Tony X.,et al. Max-Confidence Boosting With Uncertainty for Visual Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(5):1650-1659.
APA Guo, Wen,Cao, Liangliang,Han, Tony X.,Yan, Shuicheng,&Xu, Changsheng.(2015).Max-Confidence Boosting With Uncertainty for Visual Tracking.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(5),1650-1659.
MLA Guo, Wen,et al."Max-Confidence Boosting With Uncertainty for Visual Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.5(2015):1650-1659.
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