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: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Public Library of Science
2021
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| Subjects: | |
| 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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| Summary: | 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. |
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