Localizing activity groups in videos.

A latent graphical model integrating multi-target tracking, group discovery, and activity recognition is proposed.Performance of activity recognition improves when multi-target tracking and group clustering are incorporated.Group activities are better recognized based on the structured relations within the group and group-group compatibilities.Increasing the connectivity of different groups improves the overall performance.Incorporating activity information leads to robust group localization in the video. Beyond recognizing actions of individuals, activity group localization in videos aims to localize groups of persons in spatiotemporal spaces and recognize what activity the group performs. In this paper, we propose a latent graph model to simultaneously address the problem of multi-target tracking, group discovery and activity recognition. Our key insight is to exploit the contextual relations among people. We present them as a latent relational graph, which hierarchically encodes the association potentials between tracklets, intra-group interactions, correlations, and inter-group compatibilities. Our model is capable of propagating multiple evidences among different layers of the latent graph. Particularly, associated tracklets assist accurate group discovery, activity recognition can benefit from knowing the whole structured groups, and the group and activity information in turn provides strong cues for establishing coherent associations between tracklets. Experiments on five datasets demonstrate that our model achieves both significant improvements in activity group localization and competitive performance on activity recognition.