A Hybrid Pedestrian Detection System based on Visible Images and LIDAR Data

Mohamed El Ansari, Redouan Lahmyed, Alain Tremeau

2018

Abstract

This paper presents a hybrid pedestrian detection system on the basis of 3D LIDAR data and visible images of the same scene. The proposed method consists of two main stages. In the first stage, the 3D LIDAR data are classified to obtain a set of clusters, which will be mapped into the visible image to get regions of interests (ROIs). The second stage classifies the ROIs (pedestrian/non pedestrian) using SVM as classifier and color based histogram of oriented gradients (HOG) together with the local self-similarity (LSS) as features. The proposed method has been tested on LIPD dataset and the results demonstrate its effectiveness.

Paper Citation

in Harvard Style

El Ansari M., Lahmyed R. and Tremeau A. (2018). A Hybrid Pedestrian Detection System based on Visible Images and LIDAR Data. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 325-334. DOI: 10.5220/0006620803250334

in Bibtex Style

@conference{visapp18,
author={Mohamed El Ansari and Redouan Lahmyed and Alain Tremeau},
title={A Hybrid Pedestrian Detection System based on Visible Images and LIDAR Data},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={325-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006620803250334},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - A Hybrid Pedestrian Detection System based on Visible Images and LIDAR Data
SN - 978-989-758-290-5
AU - El Ansari M.
AU - Lahmyed R.
AU - Tremeau A.
PY - 2018
SP - 325
EP - 334
DO - 10.5220/0006620803250334
PB - SciTePress