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Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extract...

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Main Authors: Mohd Romlay, Muhammad Rabani, Mohd Ibrahim, Azhar, Toha, Siti Fauziah, De Wilde, Philippe, Venkat, Ibrahim
Format: Article
Language:English
English
Published: Public Library of Science 2021
Subjects:
T Technology (General)
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/91744/7/91744_Novel%20CE-CBCE%20feature%20extraction%20method.pdf
http://irep.iium.edu.my/91744/13/91744_Novel%20CE-CBCE%20feature%20extraction%20method_Scopus.pdf
http://irep.iium.edu.my/91744/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256665
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spelling my.iium.irep.917442021-09-07T02:33:25Z http://irep.iium.edu.my/91744/ Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud Mohd Romlay, Muhammad Rabani Mohd Ibrahim, Azhar Toha, Siti Fauziah De Wilde, Philippe Venkat, Ibrahim T Technology (General) TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed. Public Library of Science 2021-08-25 Article PeerReviewed application/pdf en http://irep.iium.edu.my/91744/7/91744_Novel%20CE-CBCE%20feature%20extraction%20method.pdf application/pdf en http://irep.iium.edu.my/91744/13/91744_Novel%20CE-CBCE%20feature%20extraction%20method_Scopus.pdf Mohd Romlay, Muhammad Rabani and Mohd Ibrahim, Azhar and Toha, Siti Fauziah and De Wilde, Philippe and Venkat, Ibrahim (2021) Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud. PLOS ONE, 16 (8). pp. 1-18. ISSN 1932-6203 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256665 10.1371/journal.pone.0256665
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
TK7885 Computer engineering
spellingShingle T Technology (General)
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
TK7885 Computer engineering
Mohd Romlay, Muhammad Rabani
Mohd Ibrahim, Azhar
Toha, Siti Fauziah
De Wilde, Philippe
Venkat, Ibrahim
Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud
description Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.
format Article
author Mohd Romlay, Muhammad Rabani
Mohd Ibrahim, Azhar
Toha, Siti Fauziah
De Wilde, Philippe
Venkat, Ibrahim
author_facet Mohd Romlay, Muhammad Rabani
Mohd Ibrahim, Azhar
Toha, Siti Fauziah
De Wilde, Philippe
Venkat, Ibrahim
author_sort Mohd Romlay, Muhammad Rabani
title Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud
title_short Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud
title_full Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud
title_fullStr Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud
title_full_unstemmed Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud
title_sort novel ce-cbce feature extraction method for object classification using a low-density lidar point cloud
publisher Public Library of Science
publishDate 2021
url http://irep.iium.edu.my/91744/7/91744_Novel%20CE-CBCE%20feature%20extraction%20method.pdf
http://irep.iium.edu.my/91744/13/91744_Novel%20CE-CBCE%20feature%20extraction%20method_Scopus.pdf
http://irep.iium.edu.my/91744/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256665
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score 13.223943

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