Towards real-time and low-latency video object tracking by linking tracklets of incomplete detections

This paper considers tracking of objects for video-based intrusion detection systems. Current tracking algorithms can be used for surveillance, but in that use-case, these algorithms execute with too high latency and are not suitable for real-time applications. In this paper, we propose novel techniques for tracking algorithms based on tracklets in order to improve the execution time by limiting the number of tracklets and connection updates between tracklets. An additional improvement is that tracklet clustering has previously been applied to tracking with complete detections, i.e. a detection has a one-to-one correspondence to an object, while our proposed algorithm can handle incomplete detections as well. We show that the algorithm yields only two avoidable false positives on the i-LIDS SZTE dataset. To show that the algorithm can be executed in real-time, we have measured the worst-case execution time on a popular DSP which is only 31 ms per frame. Furthermore, the tracking algorithm requires only 35 seconds to process the complete i-LIDS dataset on a PC.