Max-confidence boosting with uncertainty for visual tracking

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Wen GuoChangsheng Xu

Abstract

The 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.

References

Oct 22, 2005·IEEE Transactions on Pattern Analysis and Machine Intelligence·Robert T CollinsMarius Leordeanu
Jun 27, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·Iain MatthewsSimon Baker
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yu-Feng Li, Zhi-Hua Zhou

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Citations

Jan 24, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Junyu GaoChangsheng Xu
Sep 1, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Qing Guo Bin Wu

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