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

Tasmia Reza; Lucas Cagle; Pan Wei; John E. Ball

doi:10.1117/12.2518714

14 May 2019 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

Tasmia Reza, Lucas Cagle, Pan Wei, John E. Ball

Author Affiliations +

Proceedings Volume 10988, Automatic Target Recognition XXIX; 1098815 (2019) https://doi.org/10.1117/12.2518714
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Tasmia Reza, Lucas Cagle, Pan Wei, and John E. Ball "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", Proc. SPIE 10988, Automatic Target Recognition XXIX, 1098815 (14 May 2019); https://doi.org/10.1117/12.2518714

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