Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module

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.