  Multiple object tracking is a challenging problem in computer vision due to
difficulty in dealing with motion prediction, occlusion handling, and object
re-identification. Many recent algorithms use motion and appearance cues to
overcome these challenges. But using appearance cues increases the computation
cost notably and therefore the speed of the algorithm decreases significantly
which makes them inappropriate for online applications. In contrast, there are
algorithms that only use motion cues to increase speed, especially for online
applications. But these algorithms cannot handle occlusions and re-identify
lost objects. In this paper, a novel online multiple object tracking algorithm
is presented that only uses geometric cues of objects to tackle the occlusion
and reidentification challenges simultaneously. As a result, it decreases the
identity switch and fragmentation metrics. Experimental results show that the
proposed algorithm could decrease identity switch by 40% and fragmentation by
28% compared to the state of the art online tracking algorithms. The code is
also publicly available.
