Multi-Feature Based Multiple Particle Filters for Object Tracking

Abstract

Multiple Object tracking is a fundamental step for many computer vision applications. However, detecting and tracking objects in complex background is still a challenging task. This paper proposes an approach, which combines an improved Gaussian mixture modeling with multiple particle filters for automatic multiple targets detecting and tracking. For Gaussian mixture modeling, we make improvement on Gaussian mixture modeling in the phase of model updating by using the expectation maximization algorithm and M recent frames with weight parameters of Gaussian distributions. In the tracking stage, we integrate multiple features of targets, including color, edge and depth, into multiple particle filters to improve the performance of object tracking. By comparing with various particle filter approaches, the experimental results show that our approach can track multiple targets in complex backgrounds automatically and accurately.