# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
###### Abstract  
Multiple-object tracking (MOT) is a fundamental task in computer vision and is widely applied across various domains. However, its algorithms remain somewhat immature in practical applications. To address the challenges presented by complex scenarios featuring instances of missed detections, false alarms, and frequent target switching leading to tracking failures, we propose an approach to multi-object tracking utilizing KC-YOLO detection and an identity validity discrimination module. We have constructed the KC-YOLO detection model as the detector for the tracking task, optimized the selection of detection frames, and implemented adaptive feature refinement to effectively address issues such as incomplete pedestrian features caused by occlusion. Furthermore, we have introduced an identity validity discrimination module in the data association component of the tracker. This module leverages the occlusion ratio coefficient, denoted by \"k\", to assess the validity of pedestrian identities in low-scoring detection frames following cascade matching. This approach not only enhances pedestrian tracking accuracy but also ensures the integrity of pedestrian identities. In experiments on the MOT16, MOT17, and MOT20 datasets, MOTA reached 75.9%, 78.5%, and 70.1%, and IDF1 reached 74.8%, 77.8%, and 72.4%. The experimental results demonstrate the superiority of the methodology. This research outcome has potential applications in security monitoring, including public safety and fire prevention, for tracking critical targets. KC-YOLO; object detection; identity validity discriminator; multi-pedestrian tracking +
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accuracy and anti-jamming ability of small-target pedestrian detection. [PERSON] [10] introduced an enhanced detection-and-tracking framework with a semantic matching strategy based on deep learning. Integrating scene-aware affinity detection, this framework proves to be highly effective in alleviating challenges related to occlusion and similar appearances. [PERSON] [11] introduced an innovative approach, FairMOT, which combines CenterNet and directly embeds the Re-ID module, whose training process utilizes the cross-entropy loss function, aiding in obtaining more accurate target features. This amalgamation achieves higher precision in capturing target features, all while considering the trade-off between speed and accuracy in the multi-target tracking model. [PERSON] et al. [12] proposed a multi-pedestrian tracking algorithm using the Tracking-by-Detection framework. It addresses the diversity of human postures, appearance similarities, and occlusion in real-time road traffic scenes. The algorithm effectively leverages both pedestrian depth appearance features and motion features to establish correlations among the tracking targets, thus realizing the multi-objective target tracking of pedestrians. [PERSON] et al. [13] proposed an improved MOT approach for occlusion scenarios, combining attention mechanisms and occlusion sensing as a solution. [PERSON] [14] designed and developed a network for learning separate representations for processing occlusion re-identification guided by semantic preference object queries in a converter without strict character image alignment or any additional supervision. To better eliminate occlusion interference, they devised a Contrast Feature Learning approach to better separate hidden features from recognition features. [PERSON] et al. [15] proposed the Simple Online Real-Time Tracking (SORT) method, which fuses positional and motion information in a similarity matrix for target ID association and achieved good results in short-range matching. [PERSON] et al. [16] proposed DeepSORT based on SORT, which adds an offline pedestrian re-identification network and achieves better results in long-distance matching by merging appearance and motion information.