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

TL;DR

PlugTrack introduces an adaptive fusion framework combining Kalman filters and data-driven predictors for improved multi-object tracking, effectively handling both linear and non-linear motions without modifying existing predictors.

Contribution

It is the first framework to adaptively fuse classical and modern motion predictors via multi-perceptive analysis in multi-object tracking.

Findings

Outperforms existing methods on MOT17/MOT20 datasets.

Achieves state-of-the-art results on DanceTrack.

Demonstrates effectiveness of adaptive fusion in diverse motion scenarios.

Abstract

Multi-object tracking (MOT) predominantly follows the tracking-by-detection paradigm, where Kalman filters serve as the standard motion predictor due to computational efficiency but 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 to generate adaptive blending factors. PlugTrack achieves significant performance gains on MOT17/MOT20 and state-of-the-art on DanceTrack without modifying existing motion predictors. To the best of our knowledge, PlugTrack is the first framework to bridge classical and modern motion prediction paradigms through adaptive fusion in MOT.