TransFiner: A Full-Scale Refinement Approach for Multiple Object Tracking
Bin Sun

TL;DR
TransFiner introduces a transformer-based post-refinement framework for multiple object tracking, significantly improving tracking accuracy by effectively refining detection and motion clues based on original tracker outputs.
Contribution
The paper presents TransFiner, a novel generic attachment framework that refines MOT results using query pairs and a fusion decoder, enhancing performance in complex scenarios.
Findings
Achieves 71.5% MOTA on MOT17, surpassing previous methods.
Improves IDF1 score to 66.8%, indicating better identity preservation.
Effective in reducing misses and mistaken trajectories in crowded scenes.
Abstract
Multiple object tracking (MOT) is the task containing detection and association. Plenty of trackers have achieved competitive performance. Unfortunately, for the lack of informative exchange on these subtasks, they are often biased toward one of the two and underperform in complex scenarios, such as the inevitable misses and mistaken trajectories of targets when tracking individuals within a crowd. This paper proposes TransFiner, a transformer-based approach to post-refining MOT. It is a generic attachment framework that depends on query pairs, the bridge between an original tracker and TransFiner. Each query pair, through the fusion decoder, produces refined detection and motion clues for a specific object. Before that, they are feature-aligned and group-labeled under the guidance of tracking results (locations and class predictions) from the original tracker, finishing tracking…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Chemical Sensor Technologies · Fire Detection and Safety Systems
MethodsTrack objects as points
