# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
###### Abstract
It aims to improve the positioning accuracy under complex environmental conditions [21]. [PERSON] et al. introduced a deep learning-based real-time target-tracking method, significantly improving the tracking accuracy and robustness by incorporating a multi-scale feature fusion strategy [22]. [PERSON] et al. developed a novel multi-target tracking algorithm by integrating convolutional neural network and spatio-temporal feature extraction techniques, which handled occlusion and target overlapping [23]. [PERSON] et al. proposed a new multi-target tracking framework that combines deep reinforcement learning and multi-task learning. This framework significantly improves tracking performance in complex scenes [24]. Additionally, recent work by [PERSON] et al. proposed a transformer-based approach for MOT. This approach addresses the issue of long-range dependencies and sequence modeling by leveraging the self-attention mechanism of transformers. It significantly enhances the model's ability to track multiple targets over long sequences and crowded scenes [25]. [PERSON] et al. introduced a self-supervised learning method for MOT. This method utilizes contrastive learning to improve the feature representation of the targets. It reduces ID switches and enhances the tracking robustness in diverse and complex environments [26]. In traffic surveillance systems, cameras are typically spaced far apart with non-overlapping fields of view to reduce costs. This camera arrangement leads to several unresolved challenges: (1) Background clutter and object occlusion can result in incomplete local tracking results for a single camera; (2) Significant differences in the viewpoints of different cameras cause substantial variations in the images and surroundings, making cross-camera local tracking matching extremely difficult; (3) The number of times each target appears in different cameras and the total number of targets in the entire multi-camera network are unknown, complicating the inference of each target's global trajectory, as illustrated in Figure 1. An end-to-end vehicle tracking algorithm, FairMOT-MCVT, is proposed for complex traffic scenarios from the roadside perspective. By optimizing the feature extraction network, the algorithm significantly enhances the ability to extract features and detect vehicles that are occluded at a distance. The optimized feature extraction network introduces the Swish activation function, incorporates a multi-branch structure, and integrates the Multi-scale Dilated Attention (MSDA) mechanism [27] and the Ghost module [28] to enhance network feature extraction performance. These enhancements aim to maintain the tracking accuracy while enabling real-time operation in complex traffic scenarios over long distances, striking a balance between computational accuracy and speed. The contributions made include:  
1. An optimized feature extraction system has been proposed, accompanied by the design of a Block-Efficient module specifically tailored to detect small targets from a distance, enhancing its suitability for vehicle tracking algorithms. 2. In complex tunnel traffic scenes, a joint loss function is integrated into the roadside monitoring perspective frames, along with a framework designed to differentiate between similar vehicles and target vehicles. This framework enables the algorithm to learn more discriminative features, thereby enhancing the matching success rate, reducing ID switching, and improving the overall effectiveness of vehicle tracking. 3. The FairMOT-MCVT algorithm has been validated using the UA-DETRAC dataset [29]. The results show its effectiveness and robustness in multi-target tracking, IDF1, and FPS metrics, with a superior performance compared with other prominent techniques.