Efficient Human Action Detection Using a Transferable Distance Function

Abstract. In this paper, we address the problem of efficient human action detection with only one template. We choose the standard slidingwindow 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.

• Type: Conference
• Year: 2009
• Where: ACCV
• Authors: Weilong Yang, Yang Wang 0003, Greg Mori
• Keywords: ACCV 2009, Computer Vision, Human Action, Human Action Detection, Template Video