PlugTrack: Multi-Perceptive Motion Analysis for Adaptive Fusion in Multi-Object Tracking

Multi-object tracking (MOT) predominantly follows the tracking-by-detection paradigm, where motion prediction serves as a critical component for maintaining tracking continuity and handling occlusions. While Kalman filter have been the standard motion predictor due to their computational efficiency, they inherently fail on non-linear motion patterns. Conversely, recent data-driven motion predictors capture complex non-linear dynamics but suffer from limited domain generalization and computational overhead. Through extensive analysis, we reveal that even in datasets dominated by non-linear motion, Kalman filter outperforms data-driven predictors in up to 34\% of cases, demonstrating that real-world tracking scenarios inherently involve both linear and non-linear patterns. To leverage this complementarity, we propose PlugTrack, a novel framework that adaptively fuses Kalman filter and data-driven motion predictors through multi-perceptive motion understanding. Our approach employs multi-perceptive motion analysis through temporal patterns, prediction discrepancies, and uncertainty quantification to generate adaptive blending factors. Without architectural modifications to existing motion predictors, PlugTrack achieves significant performance gains on MOT17/MOT20, and attains state-of-the-art performance on DanceTrack. To the best of our knowledge, PlugTrack is the first framework to bridge classical and modern motion prediction paradigms through adaptive fusion in MOT.