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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.
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Alex Bewley
Zongyuan Ge
Lionel Ott
The University of Sydney
Queensland University of Technology
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Bewley et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d721d23f906f6a06bef4f0 — DOI: https://doi.org/10.1109/icip.2016.7533003