Activity Group Localization By Modeling The Relations Among Participants

Beyond recognizing the actions of individuals, activity group localization aims to determine "who participates in each group" and "what activity the group performs". In this paper, we propose a latent graphical model to group participants while inferring each group's activity by exploring the relations among them, thus simultaneously addressing the problems of group localization and activity recognition. Our key insight is to exploit the relational graph among the participants. Specifically, each group is represented as a tree with an activity label while relations among groups are modeled as a fully connected graph. Inference of such a graph is reduced into an extended minimum spanning forest problem, which is casted into a max-margin framework. It therefore avoids the limitation of high-ordered hierarchical model and can be solved efficiently. Our model is able to provide strong and discriminative contextual cues for activity recognition and to better interpret scene information for localization. Experiments on three datasets demonstrate that our model achieves significant improvements in activity group localization and state-of-the-arts performance on activity recognition.