Sparse Representation-Based Human Action Recognition Using an Action Region-Aware Dictionary

Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. Conventional vision-based systems for human action recognition require the use of segmentation in order to achieve an acceptable level of recognition effectiveness. However, generic techniques for automatic segmentation are currently not available yet. Therefore, in this paper, we propose a novel sparse representation-based method for human action recognition, taking advantage of the observation that, although the location and size of the action region in a test video clip is unknown, the construction of a dictionary can leverage information about the location and size of action regions in training video clips. That way, we are able to segment, implicitly, action and context information in a test video clip, thus improving the effectiveness of classification. That way, we are also able to develop a context-adaptive classification strategy. As shown by comparative experimental results obtained for the UCF Sports Action data set, the proposed method facilitates effective human action recognition, even when testing does not rely on explicit segmentation.