Vehicle Pose Estimation: Exploring Angular Representations

Ivan Orlov, Ivan Orlov, Marco Buzzelli, Raimondo Schettini

2024

Abstract

This paper addresses the challenge of azimuth estimation in the context of car pose estimation. Our research utilizes the PASCAL3D+ dataset, which offers a diverse range of object categories, including cars, with annotated azimuth estimations for each photograph. We introduce two architectures that approach azimuth estimation as a regression problem, each employing a deep convolutional neural network (DCNN) backbone but diverging in their output definition strategies. The first architecture employs a sin-cos representation of the car’s azimuth, while the second utilizes two directional discriminators, distinguishing between front/rear and left/right views of the vehicle. Our comparative analysis reveals that both architectures demonstrate near-identical performance levels on the PASCAL3D+ validation set, achieving a median error of 3.5◦ , which is a significant advancement in the state of the art. The minimal performance disparity between the two methods highlights their individual strengths while also underscoring the similarity in their practical efficacy. This study not only proposes effective solutions for accurate azimuth estimation but also contributes to the broader understanding of pose estimation challenges in automotive contexts. The code is available at https://github.com/vani-or/car pose estimation.

Paper Citation

in Harvard Style

Orlov I., Buzzelli M. and Schettini R. (2024). Vehicle Pose Estimation: Exploring Angular Representations. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 853-860. DOI: 10.5220/0012574300003660

in Bibtex Style

@conference{visapp24,
author={Ivan Orlov and Marco Buzzelli and Raimondo Schettini},
title={Vehicle Pose Estimation: Exploring Angular Representations},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={853-860},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012574300003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Vehicle Pose Estimation: Exploring Angular Representations
SN - 978-989-758-679-8
AU - Orlov I.
AU - Buzzelli M.
AU - Schettini R.
PY - 2024
SP - 853
EP - 860
DO - 10.5220/0012574300003660
PB - SciTePress