# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 3 Methods
### DLA-Efficient
The visualization results demonstrate that the proposed algorithm effectively tracks distant vehicles and densely occluded traffic while maintaining stable identification. Figure 11: Inference results of the FairMOT MCHS algorithm on selected videos. Figure 10: Tests on self-acquired datasets demonstrate that the detection range of the developed algorithm surpasses that of the benchmark detection algorithm. Furthermore, the tracked targets can be consistently tracked across two consecutive cameras. The algorithm also maintains a stable tracking performance even in the presence of occlusions and blurring. ## 5 Conclusions

A vehicle tracking algorithm called FairMOT-MCVT for complex traffic scenarios from a roadside view is proposed by focusing on the task of multi-vehicle tracking in computer vision from the roadside view, with deep learning as the basic technology. Regarding target detection, the Block-Efficient module designed for rigid vehicle objects enhances the feature extraction and detection capability, particularly for small-size objects. Additionally, the MSDA module is introduced to enhance the feature extraction efficiency by combining multi-scale feature fusion with attention mechanisms. For target tracking, the joint loss function, weighted \(Smooth_{L1}\) loss function, cross-entropy loss function, and time continuity loss function are designed to consider the continuity of the position and speed in the optimization process to improve the ability to distinguish vehicles with a similar appearance. In the UA-DETRAC open vehicle tracking dataset, the FairMOT-MCVT algorithm achieves optimal tracking results regarding comprehensive metrics compared with current popular tracking algorithms. Among them, MOTA reached 79.0, IDF1 reached 84.5, and FPS reached 29.03, which improved MOTA by 1.3%, IDF1 by 0.3%, and FPS by 0.68% compared with the baseline algorithm. The results show that the FairMOT-MCVT algorithm maintains continuous vehicle tracking in long-range occluded traffic scenarios, showcasing robustness and accuracy. In practical applications, the algorithm reduces equipment deployment costs while enhancing system efficiency and performance, maximizing cost-effectiveness, and meeting operational requirements. The FairMOT-MCVT algorithm, designed for multi-vehicle tracking in complex traffic scenarios from a roadside view, uses deep learning for enhanced performance. It features the Block-Efficient module for improved detection of small objects and the MSDA module for efficient feature extraction. The algorithm employs a combination of loss functions to maintain position and speed continuity, which improves vehicle distinction. On the UA-DETRAC dataset, FairMOT-MCVT achieves a MOTA of 79.0, IDF1 of 84.5, and FPS of 29.03, outperforming baseline algorithms. It demonstrates robustness and accuracy in long-range occluded scenarios, reducing equipment costs and enhancing system efficiency. Although its commendable performance in multi-target tracking tasks, the proposed algorithm still has limitations. Notably, its stability under extreme weather conditions needs validation, and the tracking accuracy may diminish in high-density traffic scenarios.