Boosting-Based Visual Tracking Using Structural Local Sparse Descriptors

This paper develops an online algorithm based on sparse representation and boosting for robust object tracking. Local descriptors of a target object are represented by pooling some sparse codes of its local patches, and an Adaboost classifier is learned using the local descriptors to discriminate target from background. Meanwhile, the proposed algorithm assigns a weight value, calculated with the generative model, to each candidate object to adjust the classification result. In addition, a template update strategy, based on incremental principal component analysis and occlusion handing scheme, is presented to capture the appearance change of the target and to alleviate the visual drift problem. Comparison with the state-of-the-art trackers on the comprehensive benchmark shows effectiveness of the proposed method.

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