关键词: 时空兴趣点筛选, 规范切, mRMR准则

Abstract: video-based human action recognition has received much attention recently in computer vision while it is a very challenging research with many applications such as automatic behavior analysis, visual surveillance and human-computer interaction. A human action recognition method based on cascaded structure is proposed in this paper. Firstly, a trajectory-based method is proposed to select the interest points detected by the Dollar detector, which is sensitive to image noise, camera movement and zooming. Therefore, the pseudo interest points in the background can be effectively excluded and the extracted features will be more relevant to action recognition. Secondly, an automatic feature selection method based on the combination of normalized cuts and mRMR criteria is used to determine a subset of the words generated by the Bag-of-Words model and construct a cascaded structure for action recognition. The purpose is to make the feature used by the cascaded structure more distinctive. Lastly, the promising experimental results validate our contribution to the improvement of accuracy in human action recognition.

Key words: space-time interest point selection, normalized cuts, mRMR criteria