Towards View-invariant Vehicle Speed Detection from Driving Simulator Images

Antonio Martínez, David Llorca, David Llorca, Iván Daza

2022

Abstract

The use of cameras for vehicle speed measurement is much more cost effective compared to other technologies such as inductive loops, radar or laser. However, accurate speed measurement remains a challenge due to the inherent limitations of cameras to provide accurate range estimates. In addition, classical vision-based methods are very sensitive to extrinsic calibration between the camera and the road. In this context, the use of data-driven approaches appears as an interesting alternative. However, data collection requires a complex and costly setup to record videos under real traffic conditions from the camera synchronized with a high-precision speed sensor to generate the ground truth speed values. It has recently been demonstrated (Martinez et al., 2021) that the use of driving simulators (e.g., CARLA) can serve as a robust alternative for generating large synthetic datasets to enable the application of deep learning techniques for vehicle speed estimation for a single camera. In this paper, we study the same problem using multiple cameras in different virtual locations and with different extrinsic parameters. We address the question of whether complex 3D-CNN architectures are capable of implicitly learning view-invariant speeds using a single model, or whether view-specific models are more appropriate. The results are very promising as they show that a single model with data from multiple views reports even better accuracy than camera-specific models, paving the way towards a view-invariant vehicle speed measurement system.

Paper Citation

in Harvard Style

Martínez A., Llorca D. and Daza I. (2022). Towards View-invariant Vehicle Speed Detection from Driving Simulator Images. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 188-195. DOI: 10.5220/0011380000003335

in Bibtex Style

@conference{kdir22,
author={Antonio Martínez and David Llorca and Iván Daza},
title={Towards View-invariant Vehicle Speed Detection from Driving Simulator Images},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={188-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011380000003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Towards View-invariant Vehicle Speed Detection from Driving Simulator Images
SN - 978-989-758-614-9
AU - Martínez A.
AU - Llorca D.
AU - Daza I.
PY - 2022
SP - 188
EP - 195
DO - 10.5220/0011380000003335
PB - SciTePress