A belief based correlated topic model for trajectory clustering in crowded video scenes

Trajectory clustering in crowded video scenes is very challenging. In this paper, we propose to use a belief based correlated topic model (BCTM) to learn discriminative middle level features for trajectory clustering. By constructing a scene prior based joint Gaussian distribution, the BCTM can uncover relations between trajectory clusters and the middle level features using a parameter estimation procedure. The method has distinct advantages over Correlated Topic Model (CTM) and Random Field Topic (RFT) model previously proposed. The inputs to the BCTM are either full trajectories or trajectory fragments obtained with an existing tracking algorithm. The output BCTM features are input to a hierarchical clustering algorithm to obtain trajectory clusters. Experiments on three benchmark datasets show that the proposed BCTM and trajectory clustering approach improves the state of the art.