Bags-of-daglets for action recognition

Recent advances in human action recognition are focusing on fine-grained action categories in large video collections. With this current trend, one of the major issues is how to handle these large collections effectively and also efficiently. In this paper, we introduce a novel action recognition method based on mid-level components and directed acyclic graphs (DAGs). DAGs, taken from different videos, are efficiently processed in order to extract a large collection of spatio-temporal sub-patterns, of increasing complexities, referred to as daglets. The latter capture local appearances as well as causal structural relationships of interacting object-parts in video sequences. The main contribution of this work includes a daglet matching procedure and a DAG kernel that captures first and high order statistics of daglets into videos. When combined with support vector machines, this DAG kernel proved to be very effective in order to capture similarity between actions in videos and to successfully achieve action recognition on a standard challenging database.