Open Access   Article

A Moving Window Search Method for Detection of Pole Like Objects Using Mobile Laser Scanner Data

A. Husain1 , R.C. Vaishya2 , Md. Omar Sarif3

1 GIS CELL, Motilal Nehru National Institute of Technology, Allahabad, India.
2 Civil Engineering, Motilal Nehru National Institute of Technology, Allahabad, India.
3 GIS CELL, Motilal Nehru National Institute of Technology, Allahabad, India.

Correspondence should be addressed to: rgi1501@mnnit.ac.in.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 1-6, Mar-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i3.16

Online published on Mar 30, 2018

Copyright © A. Husain, R.C. Vaishya, Md. Omar Sarif . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: A. Husain, R.C. Vaishya, Md. Omar Sarif, “A Moving Window Search Method for Detection of Pole Like Objects Using Mobile Laser Scanner Data”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.1-6, 2018.

MLA Style Citation: A. Husain, R.C. Vaishya, Md. Omar Sarif "A Moving Window Search Method for Detection of Pole Like Objects Using Mobile Laser Scanner Data." International Journal of Computer Sciences and Engineering 6.3 (2018): 1-6.

APA Style Citation: A. Husain, R.C. Vaishya, Md. Omar Sarif, (2018). A Moving Window Search Method for Detection of Pole Like Objects Using Mobile Laser Scanner Data. International Journal of Computer Sciences and Engineering, 6(3), 1-6.

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Abstract

Pole-Like Objects (PLOs) in the road environment located nearby the road boundary are important roadway assets. They play vital role in the road safety inspection and road planning. In present study a novel automated method for the detection of PLOs from Mobile Laser Scanner (MLS) point cloud data has been proposed. Proposed method includes four basic steps. Initially ground points are roughly filtered out from the input dataset to reduce the processing of un-necessary points; formerly local window search is performed at non-ground points to find out the concentrated point distribution. Principal Component Analysis (PCA) has been implemented at such concentrated distributed points for the identification of PLOs. In last step of proposed method knowledge based information are used for suppressing the false positives and rectifying the output. Proposed method has been tested on a MLS point cloud data of complex road environment and corresponding PLOs are detected having completeness and correctness of 91.48 % and 86.00 % respectively.

Key-Words / Index Term

LiDAR, Pole Like Objects, PCA

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