基于车载环境的交通目标跟踪

针对车载环境下小目标难以识别和相机动态移动造成的目标跟踪精度下降问题, 提出一种基于改进YOLOv5与ByteTrack的交通目标跟踪方法. 首先, 引入Transformer与加权特征金字塔(BiFPN)结构的思想重构YOLOv5检测网络, 有效捕获了特征的全局依赖关系, 缓解了深层卷积小目标信息丢失问题, 改善了车载环境下的目标检测性能. 此后, 以ByteTrack为基础提出了添加相机移动补偿的CMC-ByteTrack跟踪方法, 更精准地描述了视频前后帧的数据关联关系, 提高了相机大幅位移时的跟踪精度. 实验结果表明, 改进YOLOv5的平均检测精度(mAP)达到了82.2%, 相比原算法提高了3.9%, 与CMC-ByteTrack结合后的跟踪准确性(MOTA)相比改进前的跟踪方法提高了2.8%.

Traffic Object Tracking Based on In-vehicle Environment

This study proposes a traffic object tracking method based on improved YOLOv5 and ByteTrack to address the problem of decreased tracking accuracy caused by the difficulty in recognizing small objects in the car environment and camera movement. Firstly, the study introduces the Transformer and weighted feature pyramid network (BiFPN) structure to reconstruct the YOLOv5 detection network. This effectively captures the global dependency relationships of features, alleviates the problem of information loss for small objects in deep convolutional layers, and improves the performance of object detection in vehicular environments. Subsequently, based on ByteTrack, the study proposes the CMC-ByteTrack tracking strategy that adds camera motion compensation. The method more accurately describes the data correlation relationship between the previous and subsequent frames of the video, improving tracking accuracy during significant camera displacement. Experimental results show that the improved YOLOv5 achieves mean average precision (mAP) of 82.2%, and 3.9% increase in comparison with the original algorithm. After integration with CMC-ByteTrack, the multiple object tracking accuracy (MOTA) is increased by 2.8% in comparison with that of the original tracking method.