Unsupervised Tracking With the Doubly Stochastic Dirichlet Process Mixture Model

Abstract

We present an unsupervised tracking algorithm for human and car trajectory detection, using what is called the temporal doubly stochastic Dirichlet process (TDSDP) mixture model. The TDSDP captures the global dependence and the variation of human crowds and cars in temporal domains without the Markov assumption, making it particularly suitable for long-term tracking. Moreover, TDSDP prior can estimate the number of trajectories automatically. We first define the TDSDP based on the Cox process and then explain how to construct a TDSDP mixture model from thinning multiple Dirichlet process mixtures (DPMs) with conjugate priors. Next, a Markov chain Monte Carlo sampling inference is presented. Experimental results on synthetic and real-world data demonstrate that the proposed TDSDP mixture is superior to the DPM and the dependent Dirichlet process (DDP) in terms of topic variation modeling. PETS2001 data set experiments show that TDSDP has more robust object tracking capability over DDP based on generalized Polya urn. Low-quality fish data set experiments indicate that the TDSDP excels at solving tracking problems with insufficient features.