# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
###### Abstract  
In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles' characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network's ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements. intelligent transportation system; multi-target tracking; roadside sensing; deep learning; real-time +
Footnote †: journal: Medical and Computer Science  
0000-0001-999-3300-3300]M.M. Traditional rule-based tracking methods cannot meet the needs of the applications in complex environments. [PERSON] et al. proposed the SORT algorithm, which effectively combines Kalman filtering and the Hungarian algorithm to correlate targets and enhance real-time tracking performance [13]. Building on this concept, [PERSON] et al. introduced the DeepSORT algorithm, which retains the core idea of integrating Kalman filtering with the Hungarian algorithm from the SORT algorithm [14]. However, these methods often struggle with occlusion and overlapping targets. With advancements in deep learning, several approaches have been developed to address the limitations of traditional methods. [PERSON] et al. introduced the Jointly Learns the Detector and Embedding Model (JDE), integrating the detection and tracking models for joint learning, which proves more effective than separate models like DeepSORT [15]. Recognizing that the detection and tracking models in DeepSORT operate independently, [PERSON] et al. enhanced DeepSORT by integrating it with YOLOv5, achieving end-to-end target detection and tracking, and significantly improving tracking efficiency [16]. [PERSON] et al. devised a method for target constraint learning to enhance tracking efficiency further [17]. [PERSON] et al. proposed a pixel-guided approach that integrates joint detection and tracking tasks in MOT, enhancing the algorithm accuracy and mitigating the occlusion and overlap challenges [18]. Building on the JDE model, [PERSON] et al. [19] introduced a multi-target tracking algorithm utilizing the CenterNet [20] target detection network, transforming the problem into a midpoint-based tracking method. Despite its advancements, this method has limitations in extracting the target identification features, leading to more frequent ID switches when the targets are lost for extended periods. To address these issues, [PERSON] et al. proposed a frameless FairMOT, similar to CenterNet.