Multi-Target Tracking in Video – Part II

Abstract

This chapter presents a sequential Monte Carlo data association algorithm for tracking a varying number of interacting objects in a dynamic scene. The algorithm is based on a computational framework that consists of a hybrid measurement process, a Monte Carlo joint probabilistic data association filter, and a particle-based belief propagation algorithm. The hybrid measurement process addresses problems associated with having a varying number of objects. It does so by mixing target-driven measurements provided by a prior model of target dynamics and data-driven measurements based on a discriminative model; the Monte Carlo joint probabilistic data association filter combats the curse of dimensionality by providing a marginal posterior distribution for each individual target; and particle-based belief propagation is used to deal with occlusions among objects.Within this framework, the learning of discriminative model, the Monte Carlo joint data association filtering, and belief propagation are realized as different levels of approximation to the ‘ideal’ generative model inherent in the problem of tracking multiple visual objects. Together, these result in an efficient sequential Monte Carlo data association algorithm. The algorithm is illustrated via the tracking of multiple pedestrians in several real-life test videos. Empirical results demonstrate the algorithm’s efficiency in a number of test situations.