Multi-Target Tracking using Separated Importance Sampling Particle Filters with Joint Image Likelihood

Abstract

In visual tracking, Multi-target tracking (MTT) systems encounter the problem that unavoidably moving targets may overlap each other and the measurement process of each target becomes dependent, so we construct a tracking system with considering joint image likelihood to track recognize targets, even homogeneous ones. Also, in order to enhance the tracking performance, we extend the sequential importance sampling (SIS) particle filter with the separated importance functions for tracking each target and detection at the same time. The overall performance is validated in the experiments and shows the robustness with near real-time tracking.