Human activity recognition with beta process hidden Markov models

Trajectory-based human activity recognition aims at understanding human behaviors in video sequences, which is important for intelligent surveillance. Some existing approaches to this problem, e.g., the hierarchical Dirichlet process hidden Markov models (HDP-HMM), have a severe limitation, namely the motions cannot be shared among activities. To overcome this shortcoming, we propose a new method for modeling human trajectories based on the beta process hidden Markov models (BP-HMM). Using our technique, the number of features and the sharing schema can both be inferred automatically from training data. We develop an efficient Markov chain Monte Carlo algorithm for model training. Experiments on both synthetic and real data sets demonstrate the effectiveness of our approach.