Abstract
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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Acknowledgement
This work is supported in part by the National Natural Science Foundation of China (No. 61472036) and the Major State Basic Research Development Program of China (No. 2012CB720003).
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Liu, Y., Ma, B., Hu, H., Han, Y. (2015). Boosting-Based Visual Tracking Using Structural Local Sparse Descriptors. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_34
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