Part-based motion descriptor image for human action recognition

This paper presents a novel and efficient framework for human action recognition based on modeling the motion of human body-parts. Intuitively, a collective understanding of human body-part movements can lead to better understanding and representation of any human action. In this paper, we propose a generative representation of the motion of human body-parts to learn and classify human actions. The proposed representation combines the advantages of both local and global representations, encoding the relevant motion information as well as being robust to local appearance changes. Our work is motivated by the pictorial structures model and the framework of sparse representations for recognition. Human body-part movements are represented efficiently through quantization in the polar space. The key discrimination within each action is efficiently encoded by sparse representation for classification. The proposed framework is evaluated on both the KTH and the UCF Sport action datasets and results compared against several state-of-the-art methods.

► We propose a generative representation of the motion of human body-parts to learn and classify human actions.
► The proposed representation combines the advantages of both local and global representation.
► The key discrimination within each action is efficiently encoded by linear subspace learning or sparse representation.