## 5 Experimental Results and Analysis
### Experiments Using the Multi-Object Tracking Algorithm  
After the input of the collected video material, the results were sent to the multi-target tracking algorithm for target tracking after target detection. In this paper, vehicle images taken during the daytime, night, and rainy days were tracked, and three evaluation indexes, MOTA, MOTP, and Frag, were used to compare the algorithms' performance. The tracking performance indexes obtained from the experiments are shown in Table 8, and the tracking effects are shown in Figures 13-18. Compared with the original algorithm, after combining YOLOv5s with the improved DeepSORT, MOTA increased by 6.97%, MOTP increased by 6.91%, and Frag decreased by 24.76%. The MOTA increased by 4.56%, MOTP increased by 11.16%, and Frag decreased by 23.79% when combining the improved YOLOv5s and DeepSORT. And the MOTA increased by 9.62%, MOTP increased by 14.84%, and Frag decreased by 32.52% when combining the improved YOLOv5s algorithm with the DeepSORT algorithm. On the whole, the improved YOLOv5s algorithm has a great impact on the improvement of MOTP, and can capture objects more accurately. Additionally, the improved DeepSORT algorithm has a greater impact on the improvement of MOTA, and can better maintain the tracking trajectory. Figure 12: Comparison of detection results of different YOLOv5s models in the same image. Figure 14: Tracking results of frames 10 and 20 of the proposed algorithm in daytime. Figure 13: Tracking results of frames 10 and 20 of the original algorithm in daytime. Figure 15: Tracking results of frames 30 and 40 of the original algorithm at night. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **MOTA (\%)** & **MOTP (\%)** & **Frag** \\ \hline YOLOv5s and DeepSORT & 67.57 & 73.27 & 206 \\ YOLOv5s and Improved DeepSORT & 72.78 & 78.33 & 155 \\ Improved YOLOv5s and DeepSORT & 70.65 & 81.45 & 157 \\ Proposed Algorithm & 74.07 & 84.14 & 139 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of evaluation indicators of muti-object tracking algorithms. Internet materials were used to conduct the actual tracking effect tests. The test effects of the original YOLOv5s and DeepSORT multi-target tracking algorithms and the proposed algorithm, used during the daytime, are shown in Figures 13 and 14, respectively. It can be seen from the observation results that the original algorithm failed to detect distant vehicles due to environmental occlusion, the great distance, and other problems in frames 10 and 20. The ID of vehicle No. 8 also changed, and the overall actual tracking effect is mediocre. Compared with the original algorithm, the proposed algorithm can detect more vehicles in the same frame and can correctly detect and track vehicles with severe occlusion without ID hopping. By observing Figures 15 and 16, it can be seen that the original algorithm still fails to detect vehicles at night. Vehicle 9 in frame 30 was not tracked due to the occlusion caused by vehicle 6, and vehicle 5 in frame 40 is lost, indicating the poor tracking stability  
Figure 16: Tracking results of frames 30 and 40 of the proposed algorithm at night. Figure 17: Tracking results of frames 27 and 37 of the original algorithm, taken during a rainy day. Figure 18: Tracking results of frames 27 and 37 of the improved algorithm, taken during a rainy day. of the original algorithm. Compared with the original algorithm, the improved algorithm presented in this paper detects more vehicles at night, and the tracking results of frames 30 and 40 are remain unchanged. In frame 30, the vehicle in the lower right corner occupies a small area since only the left front wheel is exposed; neither the original algorithm nor the algorithm presented in this paper can detect it. In Figures 17 and 18, due to the influence of rain, different degrees of reflective effect appeared in the environment.