Retrospective Matching Network-Based One-Shot Multi-Object Tracking Method for UAV

Multi-Object Tracking (MOT) is promising for UAV applications but faces challenges such as small targets, occlusions, motion blur, and cluttered backgrounds. This work proposes a one-shot MOT framework with a retrospective matching architecture for UAV ground tracking. Using FairMOT as the baseline avoids two-stage redundancy, enhancing UAV deployment efficiency. The lightweight retrospective matching network, comprising feature extraction, sparse graph tracking, and a refinement module, leverages historical information to recover tracking failures, improving continuity and accuracy with low computational cost. Experiments on public benchmarks show superior tracking performance and real-time efficiency.