Multi-Pedestrian Tracking Based on Improved YOLOv8 and OC-SORT

Abstract

Multi-pedestrian tracking is an important task for the environment perception systems of autonomous vehicles. In the multi-pedestrian tracking task, mutual occlusion, posture changes, small size, and poor lighting conditions usually pose challenges. To overcome these problems, we propose a detection-based multi-pedestrian tracking method, that is, combining the improved You Only Look Once (YOLO) v8 object detection algorithm with the improved observation-centric simple online and real-time tracking (OC-SORT) algorithm. Specifically, first, we improve the YOLOv8 pedestrian detector by constructing a C2f-Clo block, introducing an explicit visual center block, and designing a lightweight shared convolutional detection head. Second, we improve the OC-SORT tracker using a height-modified intersection over union. Results of experiments on the MOT17 and MOT20 pedestrian tracking datasets show that our method achieves 7.1% and 6.5% HOTA boosts, 8.5% and 8.8% MOTA improvements, 5.5% and 6.1% MOTP increases, 5.2% and 6.7% IDF1 boosts, and 648 and 692 IDSW decreases, respectively, compared with the baseline.