Monocular Human Action Recognition Utilizing Silhouette Feature Extraction and Skin Color Detection

Exemplar-based methods have been widely used in human action recognition. To analyze human action in monocular video has always been a challenging problem, due to depth information loss and ambiguities. In this paper we presented a method applying skin color detection and then calculating relative positions of face and hands to solve self-occlusions and to eliminate ambiguities. Then we applied 2D shape analysis to classify basic human actions. Several low level features were used to describe shapes, which needs less computation and can improve recognition speed to real-time level. We testified our method on a public action database and got satisfying results.