A regression approach to zebra crossing detection based on convolutional neural networks

Abstract

Zebra crossing detection is a fundamental function of the electronic travel aid. It can locate the zebra crossing and estimate its direction to help the visually impaired to cross the road safely. In contrast to the conventional methods, a regression approach is adopted to detect zebra crossing based on convolutional neural networks. Specifically, a fixed‐size window slides across the image captured at the intersection. The image patches are sequentially fed to the logistic regression model to identify the zebra crossing. Then the image patch of zebra crossing is fed to the regression model to predict the direction. The parameters of models are optimized by the backpropagation algorithm before predictions. Compared with existing methods, the proposed method can improve the precision‐recall performance of the zebra crossing identification and reduce the root mean square error of predicted directions.