Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using Motion Trail
Sandy Ardianto, Hsueh‐Ming Hang, Wen-Huang Cheng · 2023
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 detecting the trails, we extract individual car locations at each frame using a multi-head trail extractor. Then, a multi-modal bidirectional LSTM can further improve detection accuracy. We adopt the public ICIP2020 VIP Cup dataset for training and testing. Our approach is 14 percentage points (pp) better than the state-of-the-art single-frame rotated object detector (R3Det) on the challenging nighttime video, and it is 5 FPS faster in inference speed. Our scheme achieves the AP50accuracy comparable with the state-of-the-art video object detector (MEGA), but its speed is 3 times faster, and its model size is only 28% of that of MEGA.