  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.
