Common Action Discovery and Localization in Unconstrained Videos

Similar to common object discovery in images or videos, it is of great interests to discover and locate common actions in videos, which can benefit many video analytics applications such as video summarization, search, and understanding. In this work, we tackle the problem of common action discovery and localization in unconstrained videos, where we do not assume to know the types, numbers or locations of the common actions in the videos. Furthermore, each video can contain zero, one or several common action instances. To perform automatic discovery and localization in such challenging scenarios, we first generate action proposals using human prior. By building an affinity graph among all action proposals, we formulate the common action discovery as a subgraph density maximization problem to select the proposals containing common actions. To avoid enumerating in the exponentially large solution space, we propose an efficient polynomial time optimization algorithm. It solves the problem up to a user specified error bound with respect to the global optimal solution. The experimental results on several datasets show that even without any prior knowledge of common actions, our method can robustly locate the common actions in a collection of videos.