In this paper, we address the problem of efficient human action detection with only one template. We choose the standard sliding-window approach to scan the template video against test videos, and the template video is represented by patch-based motion features. Using generic knowledge learnt from previous training sets, we weight the patches on the template video, by a transferable distance function. Based on the patch weighting, we propose a cascade structure which can efficiently scan the template video over test videos. Our method is evaluated on a human action dataset with cluttered background, and a ballet video with complex human actions. The experimental results show that our cascade structure not only achieves very reliable detection, but also can significantly improve the efficiency of patch-based human action detection, with an order of magnitude improvement in efficiency. © Springer-Verlag 2010.
CITATION STYLE
Yang, W., Wang, Y., & Mori, G. (2010). Efficient human action detection using a transferable distance function. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5995 LNCS, pp. 417–426). https://doi.org/10.1007/978-3-642-12304-7_39
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