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.

Rethink Motion Information for Occluded Person Re-Identification

“…Secondly, exploring more representative person features is crucial for the occluded person ReID framework. Multi-pedestrian occlusions [16,17] are particularly challenging compared to other types of occlusions. In these scenarios, the model's ability to distinguish the features of different pedestrians becomes even more important [18].…”

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

“…Thus, this study provides a valuable tool for medical image analysis. In another study [25], a direct comparison with the performance of StrongSORT in its advanced version was conspicuously omitted. While utilizing the KC-YOLO approach based on YOLOv5 in their detection algorithm, the study did not surpass the capabilities of more recent versions like YOLOv8.…”

“…TrackletNet [15] Robust to occlusions and identity switches Complex architecture and not real-time DMAN [29] Robust to occlusions and identity switches Not real-time SORT [30] Simple and efficient Limited robustness to occlusions and identity switches ATOM [31] Accurate tracking Complex architecture and not real-time IVDM [25] Enhanced handling of occlusions and identity switches…”