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Proceedings Paper

Automatic detection and classification of obstacles with applications in autonomous mobile robots
Author(s): Volodymyr I. Ponomaryov; Dario I. Rosas-Miranda
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Paper Abstract

Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.

Paper Details

Date Published: 29 April 2016
PDF: 11 pages
Proc. SPIE 9897, Real-Time Image and Video Processing 2016, 98970P (29 April 2016); doi: 10.1117/12.2227758
Show Author Affiliations
Volodymyr I. Ponomaryov, Instituto Politécnico Nacional (Mexico)
Dario I. Rosas-Miranda, Instituto Politécnico Nacional (Mexico)


Published in SPIE Proceedings Vol. 9897:
Real-Time Image and Video Processing 2016
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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