论文标题
使用CNN和边界盒传播的鱼眼交通监控视频的快速车辆检测和跟踪
Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation
论文作者
论文摘要
我们设计了一个快速的汽车检测和跟踪算法，用于安装在十字路口的Fisheye视频。我们使用ICIP 2020 VIP杯数据集并采用Yolov5作为对象检测基本模型。该数据集的夜间视频非常具有挑战性，基本模型的检测准确性（AP50）约为54％。我们根据框架之间的边界盒传播的概念设计了可靠的汽车检测和跟踪算法，该框架的概念分别提供了17.9个百分点（PP）和6.2 pp。分别对夜间和白天视频的基本模型的准确性提高。为了加快加速，灰度框架差用于细分市场中的中间帧，这可以使处理速度加倍。
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 (AP50) 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 6.2 pp. accuracy improvement over the base model for the nighttime and daytime videos, respectively. To speed up, the grayscale frame difference is used for the intermediate frames in a segment, which can double the processing speed.
