Multi-Target Tracking of Time-Varying Spatial Patterns

Abstract

Time-varying spatial patterns are common, but few computational tools exist for discovering and tracking multiple, sometimes overlapping, spatial structures of targets. We propose a multi-target tracking framework that takes advantage of spatial patterns inside the targets even though the number, the form and the regularity of such patterns vary with time. RANSAC-based model fitting algorithms are developed to automatically recognize (or dismiss) (il)legitimate patterns. Patterns are represented using a mixture of Markov Random Fields (MRF) with constraints (local and global) and preferences encoded into pairwise potential functions. To handle pattern variations continuously, we introduce a posterior probability for each spatial pattern modeled as a Bernoulli distribution. Tracking is achieved by inferring the optimal state configurations of the targets using belief propagation on a mixture of MRFs. We have evaluated our formulation on real video data with multiple targets containing time-varying lattice patterns and/or reflection symmetry patterns. Experimental results of our proposed algorithm show superior tracking performance over existing methods.