Multiple Feature Fusion in the Dempster-Shafer Framework for Multi-object Tracking

This paper presents a novel multiple object tracking framework based on multiple visual cues. To build tracks by selecting the best matching score between several detections, a set of probability maps is estimated by a function integrating templates using a sparse representation and color information using locality sensitive histograms. All people detected in two consecutive frames are matched with each other based on similarity scores. This last task is performed using the comparison of two models (sparse apparence and color models). A score matrix is then obtained for each model. Those scores are combined by Dempster-Shafer's combination rule. To obtain an optimal selection of the best candidate, a data association step is achieved using a greedy search algorithm. We validated our tracking algorithm on challenging publicly available video sequences and we show that we outperform recent state-of-the-art methods.