Abstract
In this paper, we present a novel visual object tracking algorithm based on ensemble of linear SVM classifiers. There are two main contributions in this paper. First of all, we propose a simple yet effective way for on-line updating linear SVM classifier, where useful “Key Frames” of target are automatically selected as support vectors. Secondly, we propose an on-line ensemble SVM tracker, which can effectively handle target appearance variation. The proposed algorithm makes better usage of history information, which leads to better discrimination of target and the surrounding background. The proposed algorithm is tested on many video clips including some public available ones. Experimental results show the robustness of our proposed algorithm, especially under large appearance change during tracking.
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Tian, M., Zhang, W., Liu, F. (2007). On-Line Ensemble SVM for Robust Object Tracking. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_33
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DOI: https://doi.org/10.1007/978-3-540-76386-4_33
Publisher Name: Springer, Berlin, Heidelberg
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