# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
###### Abstract
[PERSON] [17] proposed the BYTE data association method, which introduces low-confidence detection frames into data association matching and utilizes these low-confidence similarities between the detection frames and the tracking trajectories to mine out heavily occluded targets, thus maintaining the continuity of the tracking trajectories. While significant progress has been made in enhancing detector performance, extracting more representative features, and improving data association and matching algorithms, most tracking tasks still face common challenges in complex scenarios, such as occlusion, omissions, and distractions [18]. As a result, the robustness of existing methods is in need of improvement [19]. Based on the above problems, in order to solve complex surveillance video scenes with multiple targets tracked simultaneously, we propose a method for the simultaneous tracking of multiple pedestrians based on KC-YOLO detection and an identity validity discrimination module (IVDM). We have made improvements in both the detector and the tracker. The Convolution Block Attention Module (CBAM) [20] is introduced into the detector, utilizing attention weights to allow for a more focused and refined representation of the target features, which improves the ability of the detector to capture the effective feature information of the target, which has been decisive in improving the overall precision and accuracy of the test procedure. In the tracking process, to address the issue of tracking failure due to the short-term occlusion of the target, this method constructs an IVDM after cascade matching. The target occlusion coefficient \(k\) is calculated to discriminate whether the target identity in the low-scoring detection frame after target detection and cascade matching is valid or not and to decide whether to update its appearance features so as not to generate redundant identity data, thereby ensuring the purity of the tracked pedestrian's identity and improving the overall performance of the tracking task. To summarize, this paper's primary contributions can be outlined as follows:  
* An efficient, robust, and practical multi-pedestrian tracking method based on KC-YOLO deep detection and identity validity discrimination is proposed. This method provides an effective solution for multi-pedestrian tracking tasks in complex surveillance videos. Experimental results demonstrate its high utility, making it suitable for the long-term tracking of critical targets in various scenarios, such as public safety and firefighting. * An improved pedestrian object detector based on YOLOv5, tailored for complex environments, has been designed. This detector employs the K-means++ clustering method to select optimal detection frames and introduces the CBAM for adaptive feature refinement. The KC-YOLO network is introduced for extracting target depth features. * A pedestrian identity validity model has been developed. To address challenges such as targets reappearing after occlusion and rapid identity switches, this model assesses the identity validity of newly generated targets. Different processing strategies are applied to targets with identity validity, enhancing the tracking accuracy while ensuring the purity of pedestrian target identities. This paper is structured such that Section 2 introduces the summary of the work related to the proposed method in this study. The multi-Pedestrian Tracking Method Based on IVDM is discussed in Section 3. The experimental data and analyses the experimental results are highlighted in Section 4, and in Section 5 we summarize this study. Lastly, we discuss this study and provide an outlook for future research in Section 6.