Comparative analysis of DeepSORT, ByteTrack and StrongSORT algorithms for multi-object tracking in UAV-based video surveillance

This paper presents a comparative analysis of state-of-the-art multi-object tracking algorithms applied in UAV-based video surveillance systems. The performance results of three advanced tracking methods – DeepSORT, ByteTrack, and StrongSORT – integrated with the YOLOv8 object detector are presented. A mathematical description and experimental simulations were conducted to evaluate the accuracy, stability, and computational performance of the algorithms in dynamic and complex scenes. The obtained results indicate that the StrongSORT + YOLOv8 combination provides the best balance between accuracy and robustness, whereas the ByteTrack method demonstrates high track continuity in high-density environments. The proposed approach can be utilized to enhance the efficiency of UAV-based autonomous monitoring systems.