Enhancing Drone-Based Multi-Object Tracking Through YOLOv10 and BoostTrack Integration

Abstract

This study investigates the integration of the YOLOv10 object detection framework with the BoostTrack tracking algorithm to enhance drone-based multi-object tracking (MOT). Leveraging the lightweight YOLOv10n model, the proposed system was evaluated against larger variants (YOLOv10s and YOLOv10m) across widely recognized MOT metrics, including IDF1, MOTA, precision, recall, and identity switches. Experimental results reveal that YOLOv10n consistently outperforms its larger counterparts, achieving near-perfect IDF1 and MOTA scores, high precision and recall, and zero identity switches. These findings demonstrate that smaller, computationally efficient models can deliver robust tracking accuracy and identity preservation in dynamic aerial environments, making them well-suited for real-time drone applications. The study underscores the potential of lightweight architectures in advancing scalable, efficient, and reliable drone-based surveillance systems