Multiple object tracking based on sparse generative appearance modeling

This paper addresses multiple object tracking which still remains a challenging problem because of factors like frequent occlusions, unknown number of targets and similarity in objects' appearance. We propose a novel approach for multiple object tracking using a multiple feature framework. The main focus of the proposed method is to build a robust appearance model. The appearance model of an object is built using a color model, a sparse appearance model, a motion model and spatial information. We validated the proposed algorithm on four publicly available videos with comparisons with state-of-the-art approaches. We demonstrate that our algorithm achieves competitive results.