A cluster specific latent dirichlet allocation model for trajectory clustering in crowded videos

Trajectory analysis in crowded video scenes is challenging as trajectories obtained by existing tracking algorithms are often fragmented. In this paper, we propose a new approach to do trajectory inference and clustering on fragmented trajectories, by exploring a cluster specific Latent Dirichlet Allocation(CLDA) model. LDA models are widely used to learn middle level trajectory features and perform trajectory inference. However, they often require scene priors in the learning or inference process. Our cluster specific LDA model addresses this issue by using manifold based clustering as initialization and iterative statistical inference as optimization. The output middle level features of CLDA are input to a clustering algorithm to obtain trajectory clusters. Experiments on a public dataset show the effectiveness of our approach.