Dynamic Target Tracking and Following with UAVs Using Multi-Target Information: Leveraging YOLOv8 and MOT Algorithms

Abstract

This work presents an autonomous vision-based mobile target tracking and following system designed for unmanned aerial vehicles (UAVs) leveraging multi-target information. It explores the research gap in applying the most recent multi-object tracking (MOT) methods in target following scenarios over traditional single-object tracking (SOT) algorithms. The system integrates the real-time object detection model, You Only Look Once (YOLO)v8, with the MOT algorithms BoT-SORT and ByteTrack, extracting multi-target information. It leverages this information to improve redetection capabilities, addressing target misidentifications (ID changes), and partial and full occlusions in dynamic environments. A depth sensing module is incorporated to enhance distance estimation when feasible. A 3D flight control system is proposed for target following, capable of reacting to changes in target speed and direction while maintaining line-of-sight. The system is initially tested in simulation and then deployed in real-world scenarios. Results show precise target tracking and following, resilient to partial and full occlusions in dynamic environments, effectively distinguishing the followed target from bystanders. A comparison between the BoT-SORT and ByteTrack trackers reveals a trade-off between computational efficiency and tracking precision. In overcoming the presented challenges, this work enables new practical applications in the field of vision-based target following from UAVs leveraging multi-target information.