科研之友
微信
新浪微博
Facebook
分享链接
摘要
In recent years, action recognition has become a hot research topic in the image processing area. Some studies have shown that based on supervised learning, spatial-temporal interest points which are extracted from videos demonstrate good performance in human action recognition. In this paper, we define the attributes of human pose, and associate human pose with interest points for human action recognition. We find that interest points can be used as samplers of the particle filter method, and improve the precision of pose estimation. Human pose can be used to detect outliers in interest points, and improve the precision of action recognition. Location and density of interest points associated with human pose can also improve the precision of action recognition. Experiment results on the publicly available "Weizmann", "KTH" and "UIUC" dataset demonstrate that our method outperforms the state-of-the-art methods.
