Affiliated with RCfalse
Status已發表Published
CNN-based 3D object classification using Hough space of LiDAR point clouds
Wei Song1,3; Lingfeng Zhang1; Yifei Tian2; Simon Fong2; Jinming Liu1; Amanda Gozho1
2020-05-07
Source PublicationHuman-centric Computing and Information Sciences
Volume10Issue:1
Abstract

With the wide application of Light Detection and Ranging (LiDAR) in the collection of high-precision environmental point cloud information, three-dimensional (3D) object classification from point clouds has become an important research topic. However, the characteristics of LiDAR point clouds, such as unstructured distribution, disordered arrangement, and large amounts of data, typically result in high computational complexity and make it very difficult to classify 3D objects. Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3D object classification method using the Hough space of LiDAR point clouds to overcome these problems. First, object point clouds are transformed into Hough space using a Hough transform algorithm, and then the Hough space is rasterized into a series of uniformly sized grids. The accumulator count in each grid is then computed and input to a CNN model to classify 3D objects. In addition, a semi-automatic 3D object labeling tool is developed to build a LiDAR point clouds object labeling library for four types of objects (wall, bush, pedestrian, and tree). After initializing the CNN model, we apply a dataset from the above object labeling library to train the neural network model offline through a large number of iterations. Experimental results demonstrate that the proposed method achieves object classification accuracy of up to 93.3% on average.

Keyword3d Object Classification Lidar Point Clouds Hough Space Cnn
DOI10.1186/s13673-020-00228-8
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000533891400001
PublisherSPRINGER, ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
Scopus ID2-s2.0-85084285500
Fulltext Access
Citation statistics
Cited Times [WOS]:13   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorWei Song
Affiliation1.School of Information Science and Technology,North China University of Technology,Beijing,China
2.Department of Computer and Information Science,University of Macau,Taipa,Macao
3.Beijing Key Lab On Urban Intelligent Traffic Control Technology,Beijing,China
Recommended Citation
GB/T 7714
Wei Song,Lingfeng Zhang,Yifei Tian,et al. CNN-based 3D object classification using Hough space of LiDAR point clouds[J]. Human-centric Computing and Information Sciences,2020,10(1).
APA Wei Song,Lingfeng Zhang,Yifei Tian,Simon Fong,Jinming Liu,&Amanda Gozho.(2020).CNN-based 3D object classification using Hough space of LiDAR point clouds.Human-centric Computing and Information Sciences,10(1).
MLA Wei Song,et al."CNN-based 3D object classification using Hough space of LiDAR point clouds".Human-centric Computing and Information Sciences 10.1(2020).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wei Song]'s Articles
[Lingfeng Zhang]'s Articles
[Yifei Tian]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wei Song]'s Articles
[Lingfeng Zhang]'s Articles
[Yifei Tian]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wei Song]'s Articles
[Lingfeng Zhang]'s Articles
[Yifei Tian]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.