# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 6 Conclusions  
In this research, we have developed a multi-pedestrian tracking method based on deep detection and identity validity assessment, specifically designed for complex surveillance video scenarios where issues like target occlusion are frequent. We have constructed the KC-YOLO network as the detector, which employs the k-means++ clustering method to select the optimal target detection frames. Additionally, we have integrated a convolutional attention mechanism into the target detection algorithm, utilizing attention weights for adaptive feature refinement. This effectively suppresses  
Figure 10: Visualization of multi-pedestrian tracking results: (**a**) without IVDM; (**b**) with IVDM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MOTA/\%** & **IDFI/\%** & **MT/\%** & **ML/\%** & **IDs** \\ \hline Without IVDM & 67.1 & 69.6 & 67.6 & 9.1 & 2638 \\ With IVDM & 70.1 & 72.4 & 69.2 & 8.7 & 1689 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental ablation study of IVDM on the MOT20 dataset. secondary features to highlight crucial target characteristics, enhancing the robustness of target detection in complex scenes, where target features may become less distinct due to occlusion. The robustness of the detector has been verified through experiments. In the target tracker, we have introduced the IVDM, which performs occlusion-aware processing on pedestrian targets after feature extraction by the detector. In cases where target identities are compromised due to occlusion-induced errors, we use the occlusion coefficient \"k\" to assess the validity of the identity. Based on the output of this module, we determine whether pedestrian targets possess valid identities, influencing the decision to update the appearance features of the current dynamic target. Here are the experimental results on the MOT16 dataset: MOTA is 75.9%, and IDF1 is 74.8%. Compared to SORT, there is a 20.1% increase in MOTA and a 21.0% increase in IDF1. In comparison to CNNMTT, MOTA has improved by 10.7%, and IDF1 has seen a 12.6% improvement. When contrasted with the prototype DeepSORT method, MOTA has increased by 1.1%, and IDF1 has increased by 1.2%. The most noteworthy aspect is the substantial reduction in IDS, maintaining a high level of tracking continuity. For the MOT17 dataset, MOTA is 78.5%, and IDF1 is 77.8%. For the MOT20 dataset, the results show a MOTA of 70.1% and an IDF1 of 72.4%. When contrasted with the prototype DeepSORT method, the MOTA and IDF1 indices of our proposed method are improved by 3.3% and 4.5%. These experiments confirm that our research outperforms several advanced MOT algorithms across nearly all metrics. This study provides a stable and efficient approach to multi-pedestrian tracking in complex scenarios, significantly reducing the number of ID switches to ensure the continuity of tracking trajectories. This approach is particularly well suited for public safety and fire protection departments, enabling the continuous tracking of critical targets in crowded scenes with severe occlusion. Conceptualization, W.W. and J.L. ; methodology and validation, J.J., D.F. and D.Z. ; formal analysis, Y.L., E.G. and T.Y. ; investigation, W.W. and D.Z. ; writing--original draft preparation, J.L. and Y.L. ; writing--review and editing, T.Y. and E.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 41961063; Guilin Technology Application and Promotion Project, 2022, grant 20220138-2; and Guilin Key R&D Project, 2022, grant 20220109. Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study.