Obstacle Detection Based on Generative Adversarial Networks and Fuzzy Sets for Computer-Assisted Navigation

Abstract

Obstacle detection addresses the detection of an object, of any kind, that interferes with the canonical trajectory of a subject, such as a human or an autonomous robotic agent. Prompt obstacle detection can become critical for the safety of visually impaired individuals (VII). In this context, we propose a novel methodology for obstacle detection, which is based on a Generative Adversarial Network (GAN) model, trained with human eye fixations to predict saliency, and the depth information provided by an RGB-D sensor. A method based on fuzzy sets are used to translate the 3D spatial information into linguistic values easily comprehensible by VII. Fuzzy operators are applied to fuse the spatial information with the saliency information for the purpose of detecting and determining if an object may interfere with the safe navigation of the VII. For the evaluation of our method we captured outdoor video sequences of 10,170 frames in total, with obstacles including rocks, trees and pedestrians. The results showed that the use of fuzzy representations results in enhanced obstacle detection accuracy, reaching 88.1%.