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TrackRefine: A Plug-and-Play Decoupled Enhancement Framework for Online Multi-Object Tracking and Segmentation.

Longfei Qie¹, Chunlei Chai¹, Ruixue Wang¹

¹School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This summary is machine-generated.

TrackRefine enhances online multi-object tracking and segmentation (MOTS) by refining mask quality and improving identity association, even during occlusions. This plug-and-play framework offers a practical solution for complex tracking scenarios.

Keywords:

YOLO mask refinement multi-object tracking and segmentation multimodal memory bank plug-and-play framework trajectory association

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Existing online decoupled multi-object tracking and segmentation (MOTS) methods struggle with mask quality, memory corruption during occlusion, and unstable trajectory recovery.
These limitations hinder performance in complex real-world scenarios.

Purpose of the Study:

To propose TrackRefine, a novel plug-and-play decoupled enhancement framework for online MOTS.
To improve mask quality, identity modeling, and trajectory recovery in challenging tracking environments.

Main Methods:

Introduced a lightweight Fast GrabCut-based module for mask refinement.
Developed a multimodal long-short-term memory bank integrating appearance, semantic, and shape cues for robust identity modeling.
Implemented a progressive three-stage association strategy for stable matching and long-term trajectory recovery.

Main Results:

Achieved state-of-the-art performance on MOTS20 (69.4 sMOTSA, 82.7 MOTSA).
Demonstrated strong results on KITTI MOTS for both pedestrians and cars.
Verified plug-and-play flexibility and effectiveness across different front-end segmenters.

Conclusions:

TrackRefine offers an efficient and practical solution for online MOTS in complex scenarios.
The decoupled enhancement framework significantly improves tracking performance without altering existing architectures.