  Urban Traffic Surveillance (UTS) is a surveillance system based on a
monocular and calibrated video camera that detects vehicles in an urban traffic
scenario with dense traffic on multiple lanes and vehicles performing sharp
turning maneuvers. UTS then tracks the vehicles using a 3D bounding box
representation and a physically reasonable 3D motion model relying on an
unscented Kalman filter based approach. Since UTS recovers positions, shape and
motion information in a three-dimensional world coordinate system, it can be
employed to recognize diverse traffic violations or to supply intelligent
vehicles with valuable traffic information. We build on YOLOv3 as a detector
yielding 2D bounding boxes and class labels for each vehicle. A 2D detector
renders our system much more independent to different camera perspectives as a
variety of labeled training data is available. This allows for a good
generalization while also being more hardware efficient. The task of 3D
tracking based on 2D detections is supported by integrating class specific
prior knowledge about the vehicle shape. We quantitatively evaluate UTS using
self generated synthetic data and ground truth from the CARLA simulator, due to
the non-existence of datasets with an urban vehicle surveillance setting and
labeled 3D bounding boxes. Additionally, we give a qualitative impression of
how UTS performs on real-world data. Our implementation is capable of operating
in real time on a reasonably modern workstation. To the best of our knowledge,
UTS is to date the only 3D vehicle tracking system in a surveillance scenario
(static camera observing moving targets).
