Jointly social grouping and identification in visual dynamics with causality-induced hierarchical Bayesian model

Abstract

We concentrate on modeling the person-person interactions for group activity recognition. In order to solve the complexity and ambiguity problems caused by a large number of human objects, we propose a causality-induced hierarchical Bayesian model to tackle the interaction activity video, referring to the “what” interaction activities happen, “where” interaction atomic occurs in spatial, and “when” group interaction happens in temporal. In particular, Granger Causality has been characterized with multiple features to encode the interacting relationships between each individual in the group. Furthermore, to detect and identify the concurrent interactive simultaneously, we investigate the Relative Entropy as a metric to measure the reasonable motion dependency between two arbitrary individuals. Filtered by the causality dependency, causality motion features have been cast as the multiplicative probabilistic ingredients in Bayesian factors to formulate the compact learned latent interaction patterns aggregately that enable the power of discrimination. Experiments demonstrate our model outperforms state-of-the-art models.