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Authors: Assia Belbachir 1 ; 2 ; Antonio Ortiz 1 ; Atle Aalerud 1 and Ahmed Belbachir 1

Affiliations: 1 NORCE Norwegian Research Centre, Norway ; 2 Sorbonne Université, LIP6-UMR 7606 CNRS, France

Keyword(s): Point Cloud, LiDAR, People Detection.

Abstract: Point clouds have become significant data inputs for 3D representation, enabling accurate analysis of 3D scenes and objects. People detection from point clouds is a challenging task due to data sparsity, irregularity, occlusion, and real-time detection constraints. Existing methods based on handcrafted features or deep learning have limitations in handling occlusions, pose variations, and fast detection. This paper introduces a Random Forest classifier for people detection in point clouds, aiming to achieve both accuracy and fast performance. The point cloud data are acquired using a multi-point LiDAR system. First experiments demonstrate the effectiveness of the approach and its efficient detection compared to Multiple Layer Perceptron (MLP) in our collected Dataset.

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Paper citation in several formats:
Belbachir, A., Ortiz, A., Aalerud, A. and Belbachir, A. (2023). From Point Cloud Perception Toward People Detection. In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-670-5; ISSN 2184-2809, SciTePress, pages 520-526. DOI: 10.5220/0012258800003543

@conference{icinco23,
author={Assia Belbachir and Antonio Ortiz and Atle Aalerud and Ahmed Belbachir},
title={From Point Cloud Perception Toward People Detection},
booktitle={Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2023},
pages={520-526},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012258800003543},
isbn={978-989-758-670-5},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - From Point Cloud Perception Toward People Detection
SN - 978-989-758-670-5
IS - 2184-2809
AU - Belbachir, A.
AU - Ortiz, A.
AU - Aalerud, A.
AU - Belbachir, A.
PY - 2023
SP - 520
EP - 526
DO - 10.5220/0012258800003543
PB - SciTePress

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