Augmented Radar Points Connectivity based on Image Processing Techniques for Object Detection and Classification

Mohamed Sabry, Ahmed Hussein, Amr Elmougy, Slim Abdennadher

2022

Abstract

Perception and scene understanding are complex modules that require data from multiple types of sensors to construct a weather-resilient system that can operate in almost all conditions. This is mainly due to drawbacks of each sensor on its own. The only sensor that is able to work in a variety of conditions is the radar. However, the sparseness of radar pointclouds from open source datasets makes it under-perform in object classification tasks. This is compared to the LiDAR, which after constraints and filtration, produces an average of 22,000 points per frame within a grid map image representation of 120 x 120 meters in the real world. Therefore, in this paper, a preprocessing module is proposed to enable the radar to partially reconnect objects in the scene from a sparse pointcloud. This adapts the radar to object classification tasks rather than the conventional uses in automotive applications, such as Adaptive Cruise Control or object tracking. The proposed module is used as preprocessing step in a Deep Learning pipeline for a classification task. The evaluation was carried out on the nuScenes dataset, as it contained both radar and LiDAR data, which enables the comparison between the performance of both modules. After applying the preprocessing module, this work managed to make the radar-based classification significantly close to the performance of the LiDAR.

Paper Citation

in Harvard Style

Sabry M., Hussein A., Elmougy A. and Abdennadher S. (2022). Augmented Radar Points Connectivity based on Image Processing Techniques for Object Detection and Classification. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 535-542. DOI: 10.5220/0010860600003124

in Bibtex Style

@conference{visapp22,
author={Mohamed Sabry and Ahmed Hussein and Amr Elmougy and Slim Abdennadher},
title={Augmented Radar Points Connectivity based on Image Processing Techniques for Object Detection and Classification},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={535-542},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010860600003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Augmented Radar Points Connectivity based on Image Processing Techniques for Object Detection and Classification
SN - 978-989-758-555-5
AU - Sabry M.
AU - Hussein A.
AU - Elmougy A.
AU - Abdennadher S.
PY - 2022
SP - 535
EP - 542
DO - 10.5220/0010860600003124
PB - SciTePress