Simple Online and Realtime Tracking release_frqmxrlmhfdihbie7so7peq63e

by Alex Bewley, Zongyuan Ge, Lionel Ott, Fabio Ramos, Ben Upcroft

Released as a article .

2017  

Abstract

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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Type  article
Stage   accepted
Date   2017-07-07
Version   v2
Language   en ?
arXiv  1602.00763v2
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