# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 5 Experiments
### Experimental Results  
Our tracking performance is compared with the advanced trackers (JRMOT [20], MOTSFusion [21], SRK_ODSEA [22], Quasi-Dense [23], FANTrack [24], extraCK [25], and Point3 DT [26]) in recent years on the KITTI 2D car tracking benchmark. The results of these evaluation metrics, which are provided by the KITTI official team [17], are shown in Tables 1-3. The up arrow after each metric, which is in the first row of these tables, means the bigger the better, and vice versa. The HOTA metrics of the proposed method in detail are demonstrated in Figure 3. Based on the results, our algorithm achieves state-of-the-art performance. For instance, the HOTA, AssA, AssRe, and IDSW of our tracker rank first in these comparisons. Other metric scores also rank high on the list. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**MOT-Approach** & **HOTA\(\uparrow\)** & **DetA\(\uparrow\)** & **AssA\(\uparrow\)** & **DetRe\(\uparrow\)** & **DetPr\(\uparrow\)** & **AssRe\(\uparrow\)** & **AssPr\(\uparrow\)** & **LocA\(\uparrow\)** \\ \hline MVT & 71.00\% & 66.91\% & 75.85\% & 71.13\% & 80.72\% & 78.93\% & 85.90\% & 83.46\% \\ JRMOT [20] & 69.61\% & 73.05\% & 66.89\% & 76.95\% & 85.07\% & 69.18\% & 88.95\% & 86.72\% \\ MOTSFusion [21] & 68.74\% & 72.19\% & 66.16\% & 76.05\% & 84.88\% & 69.57\% & 85.49\% & 86.56\% \\ SRK\_ODSEA [22] & 68.51\% & 75.40\% & 63.08\% & 78.89\% & 86.00\% & 65.89\% & 87.47\% & 86.88\% \\ Quasi-Dense [23] & 68.45\% & 72.44\% & 65.49\% & 76.01\% & 85.37\% & 68.28\% & 88.53\% & 86.50\% \\ FANTrack [24] & 60.85\% & 64.36\% & 58.69\% & 69.17\% & 80.82\% & 60.78\% & 88.94\% & 84.72\% \\ extraCK [25] & 59.76\% & 65.18\% & 55.47\% & 69.21\% & 81.69\% & 61.82\% & 75.70\% & 84.30\% \\ Point3 DT [26] & 57.20\% & 55.71\% & 59.15\% & 64.66\% & 68.67\% & 63.20\% & 78.30\% & 80.07\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison results of several algorithms for eight metrics such as HOTA. The qualitative results of our visual-based multi-vehicle tracking algorithm on KITTI [16] are shown in Figure 4. The color of each bounding box indicates the target identity. The number of frames, fps and tracked objects are shown in the upper left corner of each image. Our tracker achieves about 16 frames per second (fps) in this testing video sequence. If we optimize the structure, it can be deployed on an edge AI device that is applied to advanced driver assistance systems. In frame 52, there are six cars detected, and there are five cars detected in frame 55. Compared with the two frames, we can see that the cars (with ID 54, 55, 57, and 59) are tracked successfully. It is worth noting that the vehicle with ID 52 and 61 is not detected in frame 55 due to insignificant features and thus is not tracked. However, they are on an unstructured road that does not influence driving. The car with ID 63 appears in the field of view in frame 55 and is detected in time and given a new ID. The qualitative analysis shows that our algorithm balances real-time performance and accuracy while solving illumination changes and deformations in the image sequence. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**MOT-Approach** & **TP\(\uparrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **MT Rate\(\uparrow\)** & **PT Rate\(\downarrow\)** & **ML Rate\(\downarrow\)** & **FRAG\(\downarrow\)** \\ \hline MVT & 29,656 & 4736 & 650 & 66.92\% & 24.00\% & 9.08\% & 521 \\ JRMOT [20] & 30,325 & 4067 & 787 & 70.92\% & 24.46\% & 4.62\% & 273 \\ MOTSFusion [21] & 30,100 & 4292 & 713 & 72.77\% & 24.31\% & 2.92\% & 569 \\ SRK\_ODSEA [22] & 31,062 & 3330 & 489 & 78.00\% & 19.54\% & 2.46\% & 531 \\ Quasi-Dense [23] & 30,072 & 4320 & 549 & 69.54\% & 26.61\% & 3.85\% & 567 \\ FANTrack [24] & 28,130 & 6262 & 1305 & 62.77\% & 28.46\% & 8.77\% & 701 \\ extraCK [25] & 28,463 & 5929 & 675 & 62.31\% & 31.85\% & 5.85\% & 750 \\ Point3 DT [26] & 27,955 & 6437 & 4424 & 60.46\% & 26.77\% & 12.77\% & 756 \\ \hline \hline \end{tabular}
\end{table}