# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 4 Experiments and Analyses
### Analysis of Experimental Results  
#### 4.3.1 Comparison Experiments  
We conducted the following comparative experiments on different multi-pedestrian tracking algorithms using the MOT16, MOT17, and MOT20 datasets. Table 1 shows some common algorithms for the target tracking task and the experimental results of our introduced algorithm.  
Table 1 shows that our multi-pedestrian tracking method has an absolute advantage over many methods. In terms of evaluation metrics, _MOTA_ is improved by 1.1%, and _IDF1_ is improved by 1.2%, with enhanced robustness compared to the original tracking method. It is worth noting that the IDs of our method are significantly lower than those of DeepSORT; presumably, improved models may improve the predictive power of the tracking method compared to the original model, making the tracking results more accurate and significantly improving the problem of ID hopping. The smaller number of _IDs_ makes the tracking results of the model more practical in real applications. After a series of comparisons, it leads to the conclusion that our tracking algorithm has significant advantages in all aspects of performance.  
In Table 2, we can see that our multi-pedestrian tracking method significantly improves the experimental metrics on the MOT17 dataset, with improvements of 2.1% and 4.4% for _MOTA_ and _IDF1_. We believe that the proposed IDVM is better at presenting false and erroneous identity data and thus can be applied to complex surveillance video scenarios with frequent occlusions.  
In Table 3, we compare the original DeepSORT method with the multi-pedestrian tracking method that we propose based on DeepSORT on the MOT20 dataset for comparative tests. The _MOTA_ and _IDF1_ indices of our proposed method are improved by 3.3% and 4.5%, respectively. Therefore, the improved method significantly improves the power to extract the apparent features of the pedestrian target, which leads to more accurate feature extraction and makes the overall performance of this tracking method significantly better. In addition to this, the other two metrics are also significantly improved, which illustrates that the robustness of the tracker to act on the same target during the pedestrian tracking process has been improved. Meanwhile, the pedestrian IVDM not only reduces the appearance feature contamination problem but also improves the tracking robustness for whether or not to update the appearance features after discrimination.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MOTA/\%** & **IDF1/\%** & **MT/\%** & **ML/\%** & **IDs** \\ \hline SORT [15] & 59.8 & 53.8 & 25.4 & 22.7 & 1423 \\ JDE [38] & 64.4 & 55.8 & 35.4 & 20.0 & 1544 \\ CNNMTT [39] & 65.2 & 62.2 & 32.4 & 21.3 & 946 \\ CTrackV1 [40] & 67.6 & 57.2 & 32.9 & 23.1 & 5529 \\ FairMOT [11] & 73.7 & 72.4 & 44.7 & 15.9 & 1074 \\ DeepSORT [16] & 74.8 & 73.6 & 45.2 & 15.4 & 1022 \\ Our Method & 75.9 & 74.8 & 42.5 & 18.3 & 816 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on the MOT16 dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MOTA/\%** & **IDF1/\%** & **MT/\%** & **ML/\%** & **IDs** \\ \hline SGT [41] & 52.4 & 49.5 & 21.4 & 30.7 & 8431 \\ TubeTK [42] & 63.0 & 68.6 & 31.2 & 24.2 & 4137 \\ CenterTrack [33] & 67.8 & 64.7 & 34.6 & 24.6 & 2583 \\ FairMOT [11] & 73.1 & 72.7 & 41.1 & 19.0 & 2964 \\ TransMOT [38] & 75.1 & 74.6 & 40.8 & 22.6 & 2340 \\ ByteTack [17] & 77.4 & 76.1 & 39.9 & 20.2 & 2236 \\ DeepSORT [16] & 76.4 & 73.4 & 39.1 & 21.0 & 1898 \\ Our Method & 78.5 & 77.8 & 38.6 & 19.9 & 1586 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results on the MOT17 dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MOTA/\%** & **IDF1/\%** & **MT/\%** & **ML/\%** & **IDs** \\ \hline DeepSORT [16] & 66.8 & 67.9 & 68.7 & 8.4 & 2269 \\ Our Method & 70.1 & 72.4 & 69.2 & 8.7 & 1689 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results on the MOT20 dataset.