We design a fast car detection and tracking algorithm for traffic monitoring fisheye video
mounted on crossroads. We use ICIP 2020 VIP Cup dataset and adopt YOLOv5 as the
object detection base model. The nighttime video of this dataset is very challenging, and the
detection accuracy (AP 50) of the base model is about 54%. We design a reliable car
detection and tracking algorithm based on the concept of bounding box propagation among
frames, which provides 17.9 percentage points (pp) and 7 pp accuracy improvement over …

We develop a vehicle detection and tracking scheme based on the concept of motion trails
for fisheye traffic monitoring videos. The motion trail combines the moving object traces in
several frames into one image. Because it collects information from multiple frames, the
accuracy of detecting a trail is higher than a single-frame object detector. Essentially, it
merges the detection and tracking processes into one process. In addition, a lightweight
neural net is sufficient to detect the trail, which saves computing time and memory. After …