Real-Time Distributed Multi-Object Tracking Using Multiple Interactive Trackers and a Magnetic-Inertia Potential Model

摘要

This paper presents a method which avoids the common practice of using a complex joint state-space representation and performing tedious joint data association for multiple object tracking applications. Instead, we propose a distributed Bayesian formulation using multiple interactive trackers that requires much lower complexity for real-time tracking applications. When the objects' observations do not interact with each other, our approach performs as multiple independent trackers. However, when the objects' observations exhibit interaction, defined as close proximity or partial and complete occlusion, we extend the conventional Bayesian tracking framework by modeling such interaction in terms of potential functions. The proposed "magnetic-inertia" model represents the cumulative effect of virtual physical forces that objects undergo while interacting with each other. It implicitly handles the "error merge " and "object labeling" problems and thus solves the difficult object occlusion and data association problems in an innovative way. Our preliminary simulations have demonstrated that the proposed approach is far superior to other methods in both robustness and speed