Motion-Perception Multi-Object Tracking (MPMOT): Enhancing Multi-Object Tracking Performance via Motion-Aware Data Association and Trajectory Connection

Abstract

Multiple Object Tracking (MOT) aims to detect and track multiple targets across consecutive video frames while preserving consistent object identities. While appearance-based approaches have achieved notable success, they often struggle in challenging conditions such as occlusions, motion blur, and the presence of visually similar objects, resulting in identity switches and fragmented trajectories. To address these limitations, we propose Motion-Perception Multi-Object Tracking (MPMOT), a motion-aware tracking framework that emphasizes robust motion modeling and adaptive association. MPMOT incorporates three core components: (1) a Gain Kalman Filter (GKF) that adaptively adjusts detection noise based on confidence scores, stabilizing motion prediction during uncertain observations; (2) an Adaptive Cost Matrix (ACM) that dynamically fuses motion and appearance cues during track–detection association, improving robustness under ambiguity; and (3) a Global Connection Model (GCM) that reconnects fragmented tracklets by modeling spatio-temporal consistency. Extensive experiments on the MOT16, MOT17, and MOT20 benchmarks demonstrate that MPMOT consistently outperforms state-of-the-art trackers, achieving IDF1 scores of 72.8% and 72.6% on MOT16 and MOT17, respectively, surpassing the widely used FairMOT baseline by 1.1% and 1.3%. Additionally, rigorous statistical validation through post hoc analysis confirms that MPMOT’s improvements in tracking accuracy and identity preservation are statistically significant across all datasets. MPMOT delivers these gains while maintaining real-time performance, making it a scalable and reliable solution for multi-object tracking in dynamic and crowded environments.