# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 3 Methods
### DLA-Efficient
By comparing the loss trajectories between the different branches, it can be seen that the DLA-efficient network shows practical training effects on the center point movement, height, width, and heat map, while having a strong fitting effect. A total of four ablation experiments were conducted to compare the evaluation indicators of FPS and AP. The experimental data in Table 1 show that compared with the original network, the DLA-efficient network has significant improvements and is more suitable for vehicle tracking application scenarios. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **FPS** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{s}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline Baseline & 32.5 & 36.4 & 53.9 & 38.8 & 16.4 & 40.0 & 53.4 \\ \hline +Swish & 31.8 & 37.6 & 55.4 & 39.8 & 17.2 & 41.0 & 54.1 \\ \hline +Swish + MSDA & 30.4 & 37.8 & 55.5 & 40.0 & 17.2 & 41.3 & 54.0 \\ \hline +Swish + MSDA + Block-Efficient & 30.8 & 38.1 & 56.0 & 40.5 & 17.4 & 41.5 & 55.2 \\ \hline \end{tabular}
\end{table} 
Table 1: DLA-efficient feature extraction network ablation experiment. Figure 7: Training loss comparison. First, by replacing the original activation function with the Swish activation function, while the FPS decreased by 0.7%, the other indicators were significantly improved: AP increased by 1.2%, AP increased by 1.5%, AP increased by 1.0%, AP increased by 0.8%, AP increased by 1.0%, and AP increased by 0.7%. Subsequently, the MSDA module was integrated into the front end of the network, leading to improvements in several indicators: AP increased by 0.2%, and AP increased by 0.3%, indicating that the module positively impacts small targets, such as vehicles. By incorporating the designed Block-Efficient module, the computation time is reduced, increasing the frame rate from 30.4 to 30.8 FPS and improving the remaining six indicators. Compared with the original feature extraction network DLA-34, the improved DLA-efficient network enhances the detection of small, occluded targets at a distance and effectively addresses the issue of target loss due to occlusion or distance. In summary, the DLA-efficient network developed in this article adeptly harmonizes inference speed with precision, serving as an applicable feature extraction network in advanced vehicle monitoring algorithms. To exhibit how the DLA-efficient network enhances vehicle detection skills, a segment of the UA-DETRAC dataset was depicted. Data were gathered using a heat map illustrating the test outcomes, as shown in Figure 8. The results demonstrate that the DLA-efficient network exhibits robust feature extraction capabilities across various scenes, shooting angles, occlusion levels, and lighting conditions. The network effectively reduces missed vehicle detections and minimizes center point deviation. The model size comparison is shown in Figure 9. It can be seen that DLA-efficient (max-Det = 100) had a 2.59% increase in recall and an 8% increase in accuracy compared with DLA-34. At the same time, the comparative analysis of the model volume and parameters showed that while maintaining the detection accuracy, the model volume was reduced by about 18% and the model parameters were reduced by 15%. The FairMOT-MCVT algorithm for vehicle tracking was employed in an ablation experiment to evaluate the inference speed per enhancement and the influence of five essential multi-tracking evaluation factors (FPS, MOTA, IDF1, IDS, and ML) on the model. The experimental results are shown in Table 2.

Figure 8: Comparison of heat map visualizations for algorithmic ablation. Firstly, baseline-based ablation experiments were performed using an optimized DLA-efficient feature extraction network. When contrasted with the initial network, DLA-efficient enhances the layer count, causing a minor reduction in the FPS values but leading to a 1.7% improvement in MOTA, and 0.4% in IDF1. The results show that the tracking effect is improved while meeting the real-time requirements.