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We design a fast car detection and tracking algorithm for traffic monitoringfisheye video mounted on crossroads. We use ICIP 2020 VIP Cup dataset and adoptYOLOv5 as the object detection base model. The nighttime video of this datasetis very challenging, and the detection accuracy (AP50) of the base model isabout 54%. We design a reliable car detection and tracking algorithm based onthe concept of bounding box propagation among frames, which provides 17.9percentage points (pp) and 6.2 pp. accuracy improvement over the base model forthe nighttime and daytime videos, respectively. To speed up, the grayscaleframe difference is used for the intermediate frames in a segment, which candouble the processing speed.

Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation