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Point cloud classification for detecting roadside safety attributes and distances

conference contribution
posted on 29.04.2020, 00:00 by Mingyang ZhongMingyang Zhong, Brijesh VermaBrijesh Verma, J Affum
Detecting roadside safety attributes and distances in point cloud data is a challenging task. The major problems are accurate detection of attributes and attribute centers for calculating safety distance among attributes. In this paper, we propose a point cloud classification framework for safety attributes detection. In addition, we propose an object center approximation technique for distance calculation that has been integrated into the proposed framework. The proposed framework has been evaluated on large real-world point cloud data, and the experimental results are promising. The framework achieved 100% object-wise accuracy on detecting poles and trees, while the overall point-wise accuracy on detecting all seven attributes was 86%.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

1078

End Page

1084

Number of Pages

7

Start Date

06/12/2019

Finish Date

09/12/2019

ISBN-13

9781728124858

Location

Xiamen, China

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Australian Road Research Board

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Name of Conference

IEEE Symposium Series on Computational Intelligence (SSCI 2019)