Advanced algorithms for UAV tracking of targets exhibiting start-stop and irregular motion.

This study presents breakthrough mathematical formulations for UAV tracking that achieve 56.1% HOTA accuracy for targets with start-stop and irregular motion-a 65% improvement over traditional Kalman Filter approaches. Unmanned aerial vehicles face significant challenges when tracking targets exhibiting abrupt velocity changes, intermittent stops, and nonlinear trajectories due to motion discontinuities, occlusions, and environmental noise. Conventional tracking algorithms, typically based on the assumption of constant velocity, are poorly suited for such dynamic scenarios. Our key innovation is an adaptive hybrid framework that automatically switches between motion models using innovation-based confidence metrics, maintaining tracking continuity during motion discontinuities. The framework introduces three novel technical contributions: (1) innovation-based model switching achieving 89.3% accuracy in motion transition detection, (2) enhanced α-β-γ-δ filtering with jerk compensation providing 15-25% performance improvement for irregular motion, and (3) SMART-TRACK's 3D-to-2D uncertainty propagation enabling 2.3-second recovery time compared to 5.8-second average for traditional methods. A comprehensive evaluation on benchmark datasets (VisDrone2019, UAVDT, MOT17, DanceTrack) demonstrates that hybrid approaches combining adaptive filtering with deep learning-based detection achieve superior tracking accuracy and reliability. Flow-guided margin loss specifically addresses the motion long-tailed problem, improving large motion tracking by 18.7%. Environmental robustness testing shows that advanced algorithms maintain an average accuracy of 52.3% under corruptions, compared to 34.1% for traditional methods. These findings offer practical guidance for deploying robust UAV tracking systems that can handle unpredictable target behaviors in real-world applications.