  This paper presents a novel approach for automatic recognition of human
activities for video surveillance applications. We propose to represent an
activity by a combination of category components, and demonstrate that this
approach offers flexibility to add new activities to the system and an ability
to deal with the problem of building models for activities lacking training
data. For improving the recognition accuracy, a Confident-Frame- based
Recognition algorithm is also proposed, where the video frames with high
confidence for recognizing an activity are used as a specialized local model to
help classify the remainder of the video frames. Experimental results show the
effectiveness of the proposed approach.
