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Urban Traffic Surveillance (UTS) is a surveillance system based on amonocular and calibrated video camera that detects vehicles in an urban trafficscenario with dense traffic on multiple lanes and vehicles performing sharpturning maneuvers. UTS then tracks the vehicles using a 3D bounding boxrepresentation and a physically reasonable 3D motion model relying on anunscented Kalman filter based approach. Since UTS recovers positions, shape andmotion information in a three-dimensional world coordinate system, it can beemployed to recognize diverse traffic violations or to supply intelligentvehicles with valuable traffic information. We build on YOLOv3 as a detectoryielding 2D bounding boxes and class labels for each vehicle. A 2D detectorrenders our system much more independent to different camera perspectives as avariety of labeled training data is available. This allows for a goodgeneralization while also being more hardware efficient. The task of 3Dtracking based on 2D detections is supported by integrating class specificprior knowledge about the vehicle shape. We quantitatively evaluate UTS usingself generated synthetic data and ground truth from the CARLA simulator, due tothe non-existence of datasets with an urban vehicle surveillance setting andlabeled 3D bounding boxes. Additionally, we give a qualitative impression ofhow UTS performs on real-world data. Our implementation is capable of operatingin 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).

Urban Traffic Surveillance (UTS): A fully probabilistic 3D tracking approach based on 2D detections