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Proceedings Paper

Real-time beacon identification using linear and kernel (non-linear) Support Vector Machine, Multiple Kernel Learning (MKL), and Light Detection and Ranging (LIDAR) 3D data
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Paper Abstract

The target of this research is to develop a machine-learning classification system for object detection based on three-dimensional (3D) Light Detection and Ranging (LiDAR) sensing. The proposed real-time system operates a LiDAR sensor on an industrial vehicle as part of upgrading the vehicle to provide autonomous capabilities. We have developed 3D features which allow a linear Support Vector Machine (SVM), Kernel (non-linear) SVM, as well as Multiple Kernel Learning (MKL), to determine if objects in the LiDARs field of view are beacons (an object designed to delineate a no-entry zone) or other objects (e.g. people, buildings, equipment, etc.). Results from multiple data collections are analyzed and presented. Moreover, the feature effectiveness and the pros and cons of each approach are examined.

Paper Details

Date Published: 14 May 2019
PDF: 8 pages
Proc. SPIE 10988, Automatic Target Recognition XXIX, 1098815 (14 May 2019); doi: 10.1117/12.2518714
Show Author Affiliations
Tasmia Reza, Clemson Univ. (United States)
Lucas Cagle, Mississippi State Univ. (United States)
Pan Wei,, Inc. (United States)
John E. Ball, Mississippi State Univ. (United States)

Published in SPIE Proceedings Vol. 10988:
Automatic Target Recognition XXIX
Riad I. Hammoud; Timothy L. Overman, Editor(s)

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