Object Detection as Probabilistic Set Prediction
Paper in proceeding, 2022

Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems. The development and evaluation of probabilistic object detectors have been hindered by shortcomings in existing performance measures, which tend to involve arbitrary thresholds or limit the detector’s choice of distributions. In this work, we propose to view object detection as a set prediction task where detectors predict the distribution over the set of objects. Using the negative log-likelihood for random finite sets, we present a proper scoring rule for evaluating and training probabilistic object detectors. The proposed method can be applied to existing probabilistic detectors, is free from thresholds, and enables fair comparison between architectures. Three different types of detectors are evaluated on the COCO dataset. Our results indicate that the training of existing detectors is optimized toward non-probabilistic metrics. We hope to encourage the development of new object detectors that can accurately estimate their own uncertainty. Code at https://github.com/georghess/pmb-nll.

Random finite sets

Probabilistic object detection


Object detection


Georg Hess

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Christoffer Petersson

Chalmers, Mathematical Sciences, Algebra and geometry

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13670 XVIII 550-566
978-3-031-20080-9 (ISBN)

17th European Conference on Computer Vision (ECCV)
Tel Aviv, Israel,

Deep multi-object tracking for self-driving vehicles

Wallenberg AI, Autonomous Systems and Software Program, 2021-08-01 -- 2025-08-01.

Areas of Advance



C3SE (Chalmers Centre for Computational Science and Engineering)

Subject Categories

Probability Theory and Statistics

Computer Vision and Robotics (Autonomous Systems)





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