# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 3 Methods
### DLA-Efficient
However, due to the insufficient recognition of small target vehicles within a specific range by the improved feature extraction network, the number of identifiers increases when they are occluded from each other. Furthermore, the baseline's introduction of a joint loss function results in a 0.9% rise in MOTA, a 0.2% increase in IDF1, no alteration in MT and ML, a decrease in IDS, and a minor increase in FPS. This shows that the use of an optimized feature extraction network and the introduction of a joint loss function can improve the accuracy of vehicle target discrimination and tracking, and vehicle detection and tracking from a roadside perspective have been improved. For a more compelling illustration of FairMOT-MCVT's efficacy, our proposed strategy is contrasted with four standard techniques and two of the most advanced techniques in the same testing video sequence of the UA-DETRAC dataset, as depicted in Table 3. Our algorithm improves MOTA to 79.0%, achieving the best result compared with the other algorithms. The SORT algorithm's MOTA index reached 70.3. Since the SORT algorithm's data association step relies solely on IOU technology, its real-time performance is suboptimal. In addition, its data association method is simple, resulting in a large number of ID jumps, which reduces the tracking accuracy. DeepSORT enhances the accuracy of \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **MOTA**\(\uparrow\) & **IDF1**\(\uparrow\) & **MT**\(\uparrow\) & **ML**\(\downarrow\) & **IDS**\(\downarrow\) & **FPS**\(\uparrow\) \\ \hline Baseline & 77.5 & 84.2 & 160 & 4 & 48 & 28.35 \\ \hline Baseline + DLA-efficient & 79.2 & 84.8 & 162 & 3 & 50 & 28.30 \\ \hline Baseline + Joint Loss & 78.4 & 84.4 & 160 & 4 & 46 & 28.90 \\ \hline Baseline + DLA-efficient + Joint Loss & 79.0 & 84.5 & 159 & 4 & 45 & 29.03 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparing the tracking results of mainstream algorithms in the UA-DETRAC test dataset. Figure 9: Model comparison. data association and minimizes ID jumps by creating cascading matching. Nevertheless, the algorithmic assessment index remains low because the detector's average impact is significant. FairMOT ranks just below the technique discussed in this article regarding the MOTA indicators. The method efficiently tracks both slow-moving items and pedestrians who differ significantly in their looks. Nonetheless, it overlooks the issues linked to rapidly moving entities like vehicles, resulting in the algorithm's ineffectiveness compared with this technique. CenterNet's superior detection performance indirectly enhances the precision of its tracking process. However, the algorithm is less robust due to its simple data association process and similarity to SORT. This method optimizes the network structure of vehicles and other large objects from the angle of the vehicle traffic scene and the angle of the roadside. Concurrently, implementing the joint loss function enhances the distinguishing powers of similar vehicles, reduces the identity-hopping issue for vehicles resembling each other, and optimizes the vehicle tracking algorithm from various perspectives. The RobMOT algorithm excels in multi-target tracking, achieving an IDF1 of 83.0. However, its complex computational process significantly impacts real-time performance, with an FPS of only 27.00, making it less efficient than the FairMOT-MCVT algorithm. Additionally, RobMOT exhibits deficiencies in tracking fast-moving targets. Conversely, the MTracker algorithm performs stably with long video sequences. However, its high dependence on the detector leads to performance degradation, resulting in an MOTA of only 75.5. In contrast, the FairMOT-MCVT algorithm significantly enhances the robustness and accuracy of tracking by optimizing the network structure and joint loss function, achieving a superior MOTA of 79.0. In summary, the proposed algorithm shows an excellent tracking effect compared with the six main tracking algorithms. During the evaluation process, we also encountered some limitations and challenges.