2016/02/02 by Alex Bewley, Zongyuan Ge, Lionel Ott +2 · 1 voice · 6 citations
Computer Science · #cs.CV
paper · pdf · doi:10.1109/icip.2016.7533003
Presented at ICIP 2016, code is available at https://github.com/abewley/sort
arxiv published 2016/02/02 · arxiv created 2017/07/07 · arxiv updated 2017/07/07
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers. Furthermore, due to the simplicity of our tracking method, the tracker updates at a rate of 260 Hz which is over 20x faster than other state-of-the-art trackers.