Abstract
In this paper, visual tracking is treated as an object/back-ground classification problem. Multi-scale image patches are sampled to represent object and local background. A pair of binary and one-class support vector classifiers (SVC) are trained in every scale to model the object and background discriminatively and descriptively. Then a cascade structure is designed to combine SVCs in all scales. Incremental and decremental learning schemes for updating SVCs are used to adapt the environment variation, as well as to keep away from the classic problem of model drift. Two criteria are originally proposed to quantitatively evaluate the performance of tracking algorithms against model drift. Experimental results show superior accuracy and stability of our method to several state-of-the-art approaches.
This work is supported by National Nature Science Foundation of China. Grant No. 60835004 and 60572057.
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Zhang, J., Chen, D., Tang, M. (2010). Combining Discriminative and Descriptive Models for Tracking. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_11
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DOI: https://doi.org/10.1007/978-3-642-12307-8_11
Publisher Name: Springer, Berlin, Heidelberg
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