Latent topic model-based group activity discovery

Abstract

Surveillance videos of public places often consist of group activities composed from multiple co-occurring individual activities. However, latent topic models, such as Latent Dirichlet Allocation (LDA), which have been successfully used to discover individual activities, do not discover group activities. In this paper we propose a method to discover group activities along with individual activities. We use a two layer latent structure where a latent variable is used to discover correlation of individual activities as a group activity using multinomial distribution. Each individual activity is in turn represented as a distribution over local visual features. We use a Gibbs sampling-based algorithm to jointly infer the individual and group activities. Our method can summarize not only the individual activities but also the common group activities in a video. We demonstrate the strength of our method by discovering activities and the salient correlation amongst them in real life videos of crowded public places.