# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 3 Methods
### DLA-Efficient
First, the algorithm's stability under extreme weather conditions has yet to be verified. Severe weather conditions, such as heavy rain, snow, or fog, may affect the performance of the sensors, thereby reducing the tracking accuracy. Additionally, the tracking accuracy may decrease when dealing with high-density traffic scenarios. Dense traffic flow leads to more occlusion and target overlap, increasing the difficulty of tracking. To overcome these challenges, future research can explore several improvement directions: the fusion of multi-modal data, dynamic weight adjustment, online learning mechanisms, and the integration of contextual information. Developing more advanced occlusion handling techniques will ensure that high-accuracy tracking can be maintained even in prolonged occlusion situations. Through these improvements, the FairMOT-MCVT algorithm is expected to demonstrate stronger robustness and accuracy in more complex and dynamic traffic environments, providing more reliable technical support for intelligent transportation systems. The enhancements introduced by the DLA-efficient network and FairMOT-MCVT algorithm exhibit substantial and statistically significant improvements. The advancements in crucial metrics, including AP and MOTA, transcend mere random fluctuations or noise, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MOTA** & **IDF1** & **MT** & **ML** & **IDS** & **FPS** \\ \hline SORT & 70.3 & 79.6 & 145 & 4 & 65 & 25.10 \\ \hline DeepSORT & 74.5 & 82.4 & 153 & 3 & 55 & 26.88 \\ \hline FairMOT & 77.7 & 84.2 & 160 & 4 & 47 & 28.35 \\ \hline CenterTrack & 77.0 & 84.6 & 155 & 4 & 50 & 27.55 \\ \hline RobMOT & 76.0 & 83.0 & 150 & 5 & 53 & 27.00 \\ \hline MTracker & 75.5 & 82.7 & 152 & 4 & 54 & 26.75 \\ \hline FairMOT-MCVT & 79.0 & 84.5 & 159 & 4 & 45 & 29.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tracking algorithm ablation experiment. as evidenced by a notable 1.2% increase in AP and a 1.7% boost in MOTA, which undeniably signify genuine enhancements in model performance. The consistency of these improvements across multiple experimental iterations further solidifies the reliability and validity of the findings. To substantiate the statistical significance of these enhancements, we conduct a series of rigorous t-tests comparing the performance metrics of the original and enhanced models. The resulting p-values, which consistently fall below the 0.05 threshold, confirm that the enhanced algorithm's improvements are statistically meaningful and noteworthy. ### Visualizing Results

To demonstrate the performance of the algorithms more intuitively, we performed a visual validation of the algorithms on self-selected data and compared the algorithms in the high-speed and tunnel scenarios, respectively. As shown in Figure 10, compared with the baseline algorithm, the FairMOT-MCVT algorithm can detect and track a target at longer distances and with more severe occlusions and can maintain stable tracking for the same target in two consecutive cameras. The tracking results of the FairMOT-MCVT algorithm are shown in Figure 11. In the video sequence of Sequence1, there are fewer vehicles from frame 1 to frame 560, and the small target vehicles in the distance can be tracked well and keep the ID number stable, and even the vehicle with ID number 1 can be detected and tracked under long-distance occlusion. This indicates that FairMOT-MCVT achieves superior tracking results in dense scenes, demonstrating that the weighted summation of the joint loss function effectively facilitates intra-class aggregation and inter-class separation. In frames 1030 to 1360, even if the human eye cannot recognize the tracking ID1 vehicle, our designed algorithmic model can continue tracking the target with strong robustness. In the complex congested tunnel scene sequence2, from frame 240 to frame 680, vehicle ID22 is successfully detected even though it is heavily obscured by strong reflections. In frame 1120, the target is still not lost even though it is no longer visible to the naked eye.