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Mean-Shift Blob Tracking with Adaptive Feature Selection and Scale Adaptation

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5 Author(s)
Dawei Liang ; Harbin Inst. of Technol., Harbin ; Qingming Huang ; Shuqiang Jiang ; Hongxun Yao
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When the appearances of the tracked object and surrounding background change during tracking, fixed feature space tends to cause tracking failure. To address this problem, we propose a method to embed adaptive feature selection into mean shift tracking framework. From a feature set, the most discriminative features are selected after ranking these features based on their Bayes error rates, which are estimated from object and background samples. For the selected features, a criterion is proposed to evaluate their stability for tracking and to guide feature reselection. The selected features are used to generate a weight image, in which mean shift is employed to locate the object. Moreover, a simple yet effective scale adaptation method is proposed to deal with object changing in size. Experiments on several video sequences show the effectiveness of the proposed method.

Published in:

Image Processing, 2007. ICIP 2007. IEEE International Conference on  (Volume:3 )

Date of Conference:

Sept. 16 2007-Oct. 19 2007

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