## 4 Discussion  
In order to demonstrate the effectiveness of the algorithm proposed in this paper more intuitively, Figures 7 and 8 show the tracking results of FairMOT and AMDDATrack on the test sequences of MOT15, MOT16, and MOT17 datasets. Figure 7 shows a section of tracking results of the two algorithms on the ETH-Crossing sequence of the MOT15 dataset. The scene has complex pedestrian flow, changing backgrounds, a large number of pedestrians, and large appearance changes depending on the shooting angle. With regard to the numbers marked on the pedestrians in the video, when tracking with the FairMOT algorithm, the algorithm assigns ID numbers 1 and 10 to the middle-aged couple (Figure 7a), and when the boy wearing a helmet crosses through the pedestrians, an occlusion is gradually created (Figure 7b). Because of the occlusion, the trajectory is \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **AP(\%)** \\ \hline SSD & 31.2 \\ YOLOV4 & 43.5 \\ Faster RCNN & 34.7 \\ Mask-RCNN & 39.8 \\ CenterNet & 47 \\ CenterNet\_ours & 47.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with mainstream detection networks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **MOTA** & **IDF** & **IDs** & **FN** & **FP** \\ \hline Tube\_TK(one-shot) [41] & 63.0 & 58.6 & 413 & 177,483 & 27,060 \\ CSTrack(one-shot) [42] & 74.9 & 72.6 & 3567 & 114,303 & 23,847 \\ DeepSORT(two-stage) & 60.3 & 61.2 & 2442 & 185,301 & 36,111 \\ TransTrack(one-shot) [43] & 65.8 & 56.9 & 5355 & 163,683 & 24,000 \\ FairMOT(one-shot) & 73.7 & 72.3 & 3303 & 117,477 & 27,507 \\ AMDDATrack(one-shot) & 74.3 & 75.3 & 2056 & 84,932 & 26,773 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Tracking results on the MOT17 dataset. interrupted, and the two pedestrians are reassigned new ID numbers, 16 and 19, at frame 126, (Figure 7d), that is, an ID switch occurs, and the couple has a total of six switches in the whole video sequence. The AMDDATrack algorithm initially assigns the couple ID numbers 1 and 7, (Figure 7e), and when the boy with the helmet gradually obscured the lady with number ID 1, there is a brief interruption in the trajectory, but the tracking frame is quickly retrieved at frame 120. The ID numbers of both people do not change after the end of masking, and no ID switches occur in the whole video sequence, which maintains accurate and stable tracking the whole time (Figure 7h). The tracking effects of the FairMOT and AMDDATrack algorithms proposed in this paper on the 05-SDP video sequence in the MOT17 dataset are shown in Figure 8. When using the FairMOT algorithm to track the video sequence, the pedestrian in the center of  
Figure 8: Tracking results of 05-SDP sequence. (**a**) FairMOT results in frame 97. (**b**) FairMOT results in frame 120. (**c**) FairMOT results in frame 198. (**d**) AMDDATrack results in frame 97. (**e**) AMDDATrack results in frame 120. (**f**) AMDDATrack results in frame 198. Figure 7: Tracking results of ETH-Crossing sequence. (**a**) FairMOT results in frame 92. (**b**) FairMOT results in frame 111. (**c**) FairMOT results in frame 120. (**d**) FairMOT results in frame 126. (**e**) AMDDATrack results in frame 92. (**f**) AMDDATrack results in frame 111. (**g**) AMDDATrack results in frame 120. (**h**) AMDDATrack results in frame 126.  
the frame is detected at frame 97, and the algorithm assigns him ID number 10 (Figure 8a). Subsequently, the video is continuously obscured by vehicles and pedestrians to different degrees (Figure 8b), and the pedestrian is reassigned ID number 16 at frame 198, i.e., the trajectory is interrupted, generating an ID switch, A total of three ID switches are generated in the whole video, and track interruption is generated at frame 214. When the AMDDATrack algorithm is used, the pedestrian in the center of the frame is initially assigned ID number 8, (Figure 8d); the algorithm can still track him continuously and stably after vehicles and other pedestrians obscure him, (Figure 8f).