A Markov clustering topic model for mining behaviour in video

This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clus-tering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Alloca-tion), and overcomes their drawbacks on accuracy, robust-ness and computational efficiency. Specifically, our model profiles complex dynamic scenes by robustly clustering vi-sual events into activities and these activities into global behaviours, and correlates behaviours over time. A col-lapsed Gibbs sampler is derived for offline learning with unlabeled training data, and significantly, a new approxi-mation to online Bayesian inference is formulated to enable dynamic scene understanding and behaviour mining in new video data online in real-time. The strength of this model is demonstrated by unsupervised learning of dynamic scene models, mining behaviours and detecting salient events in three complex and crowded public scenes. 1