Ground-Target Tracking in Multiple Cameras Using Collaborative Particle Filters and Principal Axis-Based Integration

Abstract

This paper presents a novel approach to tracking ground targets in multiple cameras. A target is tracked not only in each camera but also in the ground plane by individual particle filters. These particle filters collaborate in two different ways. First, the particle filters in each camera pass messages to those in the ground plane where the multi-camera information is integrated by intersecting the targets' principal axes. This largely relaxes the dependence on precise foot positions when mapping targets from images to the ground plane using homographies. Second, the fusion results in the ground plane are then incorporated by each camera as boosted proposal functions. A mixture proposal function is composed for each tracker in a camera by combining an independent transition kernel and the boosted proposal function. The general framework of our approach allows us to track individual targets distributively and independently, which is of potential use in case that we are only interested in the trajectories of a few key targets and that we cannot track all the targets in the scene simultaneously.