NDLI: Feature-based detection and classification of moving objects using LiDAR sensor

Content Provider	IET Digital Library
Author	Guo, Ziming Cai, Baigen Jiang, Wei Wang, Jian
Abstract	Detection and classification of moving objects is essential for autonomous driving. To tackle this problem, this paper proposes an object classification method at detection level using a single LiDAR sensor. The aim is to extract and classify all the moving vehicles, bicyclists, and pedestrians in front of the sensor. First, the point clouds are segmented to produce distinct groups of points representing different objects, where the line segments are extracted. A segmentation combination strategy is conducted to address the over-segmentation caused by occlusion. Then, considering the object geometry and reflection intensity, several features for classification are defined and extracted from different hand-labelled object classes. Reference ranges of all the features are generated on a set of experiment samples. Finally, the object class can be decided by checking if its features match with the existing feature reference ranges of a certain class. The proposed method was evaluated using datasets gathered by the vehicle demonstrator, and the experiment results show considerable improvement of classification performance compared to an existing object classification method based on the object matching framework.
Starting Page	1088
Ending Page	1096
Page Count	9
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 7, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5291
Journal	IET Intelligent Transport Systems
Publisher Date	2019-03-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Autonomous Driving Computer Vision And Image Processing Technique Feature Extraction Feature Reference Ranges Feature-based Classification Feature-based Detection Hand-labelled Object Class Image Classification Image Matching Image Recognition Image Segmentation Moving Vehicle Object Classification Object Detection Object Geometry Object Matching Framework Optical Radar Point Cloud Segmentation Reflection Intensity Single LiDAR Sensor Vehicle Demonstrator
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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