Multiple object tracking via a two-way confidence-based correspondence algorithm

In this paper an efficient object correspondence algorithm is presented for tracking multiple objects in dynamic environments is proposed. It is assumed that objects can be added to the scene, removed from the environment, or occluded by other objects. The proposed algorithm benefits from two key features including a confidence measure and a two-way matching mechanism to improve the correspondence accuracy. Unlike the traditional methods which solve the correspondence problem by matching new objects and then removing the incorrect ones, our algorithm avoids establishing invalid correspondences considering a confidence measure. Also, we introduce a two-way correspondence algorithm consisting of forward matching and backward matching. As a result, the track of objects is expanded both from the head and tail using new objects and previous unmatched objects respectively. The proposed method has been applied to synthetic data, and the results show the efficiency and reliability of the method against a large number of objects.