Back to Search Start Over

UTrack: Multi-Object Tracking with Uncertain Detections

Authors :
Solano-Carrillo, Edgardo
Sattler, Felix
Alex, Antje
Klein, Alexander
Costa, Bruno Pereira
Rodriguez, Angel Bueno
Stoppe, Jannis
Publication Year :
2024

Abstract

The tracking-by-detection paradigm is the mainstream in multi-object tracking, associating tracks to the predictions of an object detector. Although exhibiting uncertainty through a confidence score, these predictions do not capture the entire variability of the inference process. For safety and security critical applications like autonomous driving, surveillance, etc., knowing this predictive uncertainty is essential though. Therefore, we introduce, for the first time, a fast way to obtain the empirical predictive distribution during object detection and incorporate that knowledge in multi-object tracking. Our mechanism can easily be integrated into state-of-the-art trackers, enabling them to fully exploit the uncertainty in the detections. Additionally, novel association methods are introduced that leverage the proposed mechanism. We demonstrate the effectiveness of our contribution on a variety of benchmarks, such as MOT17, MOT20, DanceTrack, and KITTI.<br />Comment: Accepted for the ECCV 2024 Workshop on Uncertainty Quantification for Computer Vision

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2408.17098
Document Type :
Working Paper