loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Youssef Shoeb 1 ; 2 ; Nazir Nayal 3 ; Azarm Nowzad 1 ; Fatma Güney 3 and Hanno Gottschalk 2

Affiliations: 1 Continental AG., Germany ; 2 Technische Universität Berlin, Germany ; 3 Koç University, Turkey

Keyword(s): Road Obstacle Detection, Likelihood Ratio, Applications of Foundational Models.

Abstract: Detecting road obstacles is essential for autonomous vehicles to navigate dynamic and complex traffic environments safely. Current road obstacle detection methods typically assign a score to each pixel and apply a threshold to generate final predictions. However, selecting an appropriate threshold is challenging, and the per-pixel classification approach often leads to fragmented predictions with numerous false positives. In this work, we propose a novel method that leverages segment-level features from visual foundation models and likelihood ratios to predict road obstacles directly. By focusing on segments rather than individual pixels, our approach enhances detection accuracy, reduces false positives, and offers increased robustness to scene variability. We benchmark our approach against existing methods on the RoadObstacle and LostAndFound datasets, achieving state-of-the-art performance without needing a predefined threshold.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 216.73.216.108

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Shoeb, Y., Nayal, N., Nowzad, A., Güney, F. and Gottschalk, H. (2025). Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3; ISSN 2184-4321, SciTePress, pages 306-315. DOI: 10.5220/0013126700003912

@conference{visapp25,
author={Youssef Shoeb and Nazir Nayal and Azarm Nowzad and Fatma Güney and Hanno Gottschalk},
title={Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={306-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013126700003912},
isbn={978-989-758-728-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios
SN - 978-989-758-728-3
IS - 2184-4321
AU - Shoeb, Y.
AU - Nayal, N.
AU - Nowzad, A.
AU - Güney, F.
AU - Gottschalk, H.
PY - 2025
SP - 306
EP - 315
DO - 10.5220/0013126700003912
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

COPE

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar