Abstract:
Action recognition in a video plays an important role in computer vision and finds many applications in areas such as surveillance, sports, and elderly monitoring. Existing methods mostly rely on stationary backgrounds. Action recognition in dynamic backgrounds typically requires standard preprocessing steps such as motion compensation, background modeling, moving object detection and object recognition. The errors of the motion compensation step and background modelling increase the mis-detections. Therefore action recognition in dynamic background is challenging. In this paper, we use a combination of pose characterized by a silhouette and optic flows synthesized into a histogram. This enables us to classify the movement of the actor
versus movement of the background. We use four background models to extract the silhouette from the frame. We use SVM to recognize actions, according to several evaluation protocols. We perform several experiments and compare over a diverse set of
challenging videos, including the new Change Detection Challenge Dataset. Our results perform better than existing methods.
