A Greedy Searching Algorithm for Multiple Object Tracking and Occlusion Handling

Abstract This paper presents a novel real-time multiple object tracking algorithm, which contains three parts: region correlation based foreground segmentation, merging-splitting based data association and greedy searching based occluded object localization. The main characteristics of the proposed algorithm are summarized as follows: 1) the multiple object tracking and occlusion handling problem is successfully changed into an image classification problem with prior knowledge of object number and feature; 2) a highly efficient greedy searching method is presented to meet real-time capability; 3) it has good performance in expansibility, and it has no constraints about the number of occluded objects, the occlusion ratio and the object′s motion model. Experiment results with hand labeled IBM database demonstrate that the method is effective and efficient