论文标题
基于Yolov7和Deepsort的视频对象跟踪
Video object tracking based on YOLOv7 and DeepSORT
论文作者
论文摘要
多对象跟踪（MOT）是计算机视觉领域的重要技术，该技术广泛用于自动驾驶，智能监控，行为识别和其他方向。在基于深度学习的当前流行MOT方法中，基于检测的跟踪（DBT）是行业中最广泛使用的，它们的性能取决于其对象检测网络。目前，性能良好，使用最广泛的DBT算法是Yolov5-Deepsort。受Yolov5-Deepsort的启发，其在对象检测方面的性能更好，我们将Yolov7作为对象检测部分应用于DeepSort，并提出Yolov7-Deepsort。经过实验评估后，与以前的Yolov5-Deepsort相比，Yolov7-Deepsort的表现更好地跟踪准确性。
Multiple object tracking (MOT) is an important technology in the field of computer vision, which is widely used in automatic driving, intelligent monitoring, behavior recognition and other directions. Among the current popular MOT methods based on deep learning, Detection Based Tracking (DBT) is the most widely used in industry, and the performance of them depend on their object detection network. At present, the DBT algorithm with good performance and the most widely used is YOLOv5-DeepSORT. Inspired by YOLOv5-DeepSORT, with the proposal of YOLOv7 network, which performs better in object detection, we apply YOLOv7 as the object detection part to the DeepSORT, and propose YOLOv7-DeepSORT. After experimental evaluation, compared with the previous YOLOv5-DeepSORT, YOLOv7-DeepSORT performances better in tracking accuracy.
