Simple Online and Realtime Tracking
release_mfq5c5vy6jhojk3mnzsbg7giem
by
Alex Bewley,
Zongyuan Ge,
Lionel Ott,
Fabio Ramos,
Ben Upcroft
2016
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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