Object detection and classification for outdoor walking guidance system

Abstract

In this paper, we present an object detection and classification method for OpenEyes-II. OpenEyes-II is a walking guidance system that helps the visually impaired to respond naturally to various situations that can occur in unrestricted natural outdoor environments during walking and reaching the destination. Object detection and classification is requisite for implementing obstacle and face detection which are major parts of a walking guidance system. It can discriminate pedestrian from obstacles, and extract candidate regions for face detection and recognition. We have used stereo-based segmentation and SVM (Support Vector Machines), which has superior classification performance in binary classification case such like object detection. The experiments on a large number of street scenes demonstrate the effectiveness of the proposed method.