Directed Acyclic Graph Kernels for Action Recognition

One of the trends of action recognition consists in extracting and comparing mid-level features which encode visual and motion aspects of objects into scenes. However, when scenes contain high-level semantic actions with many interacting parts, these mid-level features are not sufficient to capture high level structures as well as high order causal relationships between moving objects resulting into a clear drop in performances. In this paper, we address this issue and we propose an alternative action recognition method based on a novel graph kernel. In the main contributions of this work, we first describe actions in videos using directed a cyclic graphs (DAGs), that naturally encode pair wise interactions between moving object parts, and then we compare these DAGs by analyzing the spectrum of their sub-patterns that capture complex higher order interactions. This extraction and comparison process is computationally tractable, resulting from the a cyclic property of DAGs, and it also defines a positive semi-definite kernel. When plugging the latter into support vector machines, we obtain an action recognition algorithm that overtakes related work, including graph-based methods, on a standard evaluation dataset.