Into the Fog: Evaluating Robustness of Multiple Object Tracking


Nadezda Kirillova (Technische Universität Graz), Muhammad Jehanzeb Mirza (Massachusetts Institute of Technology), Horst Bischof (Graz University of Technology), Horst Possegger (Graz University of Technology)
The 35th British Machine Vision Conference
PDFPosterVideo (Right click to download)Supplementary

Abstract

State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear weather scenarios, overlooking adverse atmospheric conditions such as fog, haze, smoke and dust. As a result, the robustness of trackers against these challenging conditions remains underexplored. To address this gap, we introduce physic-based volumetric fog simulation method for arbitrary MOT datasets, utilizing frame-by-frame monocular depth estimation and a fog formation optical model. We enhance our simulation by rendering both homogeneous and heterogeneous fog and propose to use the dark channel prior method to estimate atmospheric light, showing promising results even in night and indoor scenes. We present the leading benchmark MOTChallenge (third release) augmented with fog (smoke for indoor scenes) of various intensities and conduct a comprehensive evaluation of MOT methods, revealing their limitations under fog and fog-like challenges.

Citation

@inproceedings{Kirillova_2024_BMVC,
author    = {Nadezda Kirillova and Muhammad Jehanzeb Mirza and Horst Bischof and Horst Possegger},
title     = {Into the Fog: Evaluating Robustness of Multiple Object Tracking},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0362.pdf}
}


Copyright © 2024 The British Machine Vision Association and Society for Pattern Recognition
The British Machine Vision Conference is organised by The British Machine Vision Association and Society for Pattern Recognition. The Association is a Company limited by guarantee, No.2543446, and a non-profit-making body, registered in England and Wales as Charity No.1002307 (Registered Office: Dept. of Computer Science, Durham University, South Road, Durham, DH1 3LE, UK).

Imprint | Data Protection