  Detecting and tracking vehicles in urban scenes is a crucial step in many
traffic-related applications as it helps to improve road user safety among
other benefits. Various challenges remain unresolved in multi-object tracking
(MOT) including target information description, long-term occlusions and fast
motion. We propose a multi-vehicle detection and tracking system following the
tracking-by-detection paradigm that tackles the previously mentioned
challenges. Our MOT method extends an Intersection-over-Union (IOU)-based
tracker with vehicle re-identification features. This allows us to utilize
appearance information to better match objects after long occlusion phases
and/or when object location is significantly shifted due to fast motion. We
outperform our baseline MOT method on the UA-DETRAC benchmark while maintaining
a total processing speed suitable for online use cases.
