A particle filter approach for multi-target tracking

The problem of tracking multiple objects poses a number of challenges due to the ambiguity of the observations and the presence of partial or complete occlusions. This paper introduces a novel extension to the Particle Filter algorithm for tracking multiple objects with a vision system. The presented approach instantiates separate particle filters for each object and explicitly handles partial and complete occlusion for non- transparent objects, as well as the instantiation and removal of filters in case new objects enter the scene or previously tracked objects are removed. As opposed to single particle filters or mixture particle filter approaches which estimate a single multi-modal distribution, the proposed filter extension allows the continued tracking of objects through occlusion situations as well as the tracking of multiple objects of different types. To allow for the handling of occlusions without an increase in computational complexity beyond the one of the Mixture Particle Filter, the approach presented here addresses occlusions by projecting particles into the image space and back into the particle space, thus avoiding the use of a joint distribution. To present qualitative results, experiments were performed using color-based tracking of multiple objects of different and identical colors. The experiments demonstrate that the Particle filters implemented using the proposed method effectively and precisely track multiple targets and can successfully instantiate and remove filters of objects that enter or leave the image area.