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.