## 1 Introduction  
At present, vehicles are a necessity; they greatly facilitate people's travel and also greatly increase the productivity of society and mankind. According to the statistics of Hedges company's automotive market research and the digital marketing consulting company [1], as shown in Table 1, there were about 1.446 billion cars on the earth in 2022. The three continents with the most cars are Asia, Europe, and North America, and the most populous, Asia, has the most vehicles, with a total of approximately 531 million vehicles. The two countries with the highest levels of vehicle ownership are China and the United States, with approximately 310 million and 280 million vehicles, respectively. However, the continuous growth in the means of transportation has led the transportation system to face many problems, such as traffic congestion and traffic accidents. To solve these problems, it is necessary to track the vehicles on the road in real time to determine the traffic flow and adjust the traffic system in a timely manner [2, 3]. Object tracking has important application value in the field of computer vision and artificial intelligence [4, 5, 6, 7], and DeepSORT, as a deep-learning-based object tracking algorithm, has attracted widespread attention from scholars worldwide [8, 9, 10, 11, 12, 13, 14, 15]. In recent research, deep learning algorithms have made great progress in pedestrian target detection, but the accuracy of vehicle tracking algorithms still needs to be improved, especially in complex scenes, such as those containing occlusion, lighting changes and scale changes. In the research field based on DeepSORT, [PERSON] et al. introduced the channel-relation-aware global attention (RGA-C) and spatial relation-aware global attention (RGA-S) mechanisms into the network structure, and the Hard-Negative Mining Way was introduced into the basic triple loss to improve the accuracy of DeepSORT [7]. [PERSON] et al. combined gray and RGB features using Iterative Deep Aggregation (IDA) to reduce the error rate of the model [10]. [PERSON] et al. selected the OSNet full-scale network to optimize the shallow residual network and improve the apparent feature extraction ability, and used the Complete Intersection over Union (CIoU) matching method to judge the matching degree between the detection frame and boundary regression [11]. [PERSON] et al. designed a dual-track prediction mechanism comprising a kernel correlation filter and Kalman filter and formed a prediction-track-calibration system with cascade matching in DeepSORT to make data association more reliable [12]. In the process of vehicle feature matching, [PERSON] et al. extracted Haar-like features to match the light and dark change information of vehicles to improve the object-matching accuracy. Based on the DeepSORT re-recognition network, the improved ResNet13 was adopted as the backbone network of feature extraction, and SENet was added to adjust the feature weights of different channel dimensions [13]. [PERSON] used GhostNetV1 to replace the re-recognition network in DeepSORT to generate the appearance features of pedestrians and improve the performance of the pedestrian re-recognition network. Then, the Hungarian algorithm was adopted to perform the optimal matching between the detection frame and the prediction frame, and DIOU was adopted to replace IOU (crossover ratio) in the second matching of the unmatched detection frame and improve the tracking performance of DeepSORT network [14]. This work aims to increase the detection speed of the target detection model You Only Look Once 5s version (YOLOv5s) and improve the detection accuracy of this model and the target tracking accuracy of the multi-target tracking algorithm DeepSORT by improving the backbone network of the YOLOv5s model, optimizing the KF target location prediction algorithm, and enhancing DeepSORT's appearance feature extraction network. This is applied to vehicle multi-object detection and tracking.