Zebra-Crossing Detection and Recognition Based on Flood Fill Operation and Uniform Local Binary Pattern

Abstract

Zebra-crossing region detection from a zebracrossing

image is an important and demanding task to support

visually impaired people to navigate the street crossing safely in

the outdoor environments. In this paper, a zebra-crossing

detection and recognition method is presented where zebracrossing

region is detected by employing the image processing

techniques such as adaptive histogram equalization, flood fill

operation, and Hough transforms and is recognized through the

uniform local binary pattern with support vector machine (SVM)

classifier. For that, the contrast and sharpness of the zebracrossing

image is improved by the adaptive histogram equalization

if the image’s intensity value is less than an empirical threshold

value. After that, the pre-processed zebra-crossing image is

converted to the binary image by using the Otsu’s method.

Furthermore, the morphological and flood fill operations are

applied to the binary image to extract the largest candidate object.

The edges of the largest candidate object are detected by utilizing

the canny operator. From the edges, the potential longest

horizontal edges are estimated by eliminating the vertical edges

using four connected method and filtering the small edges using

statistical threshold procedure. Finally, the potential parallel

horizontal edges are justified as zebra-crossing edge lines by

drawing the Hough lines and detect the zebra-crossing region of

interest (ROI). Then, the SVM classifier is applied to the detected

ROI region to recognize the zebra-crossing region where,

rotational invariant uniform local binary pattern is utilized to

extract the features of candidate region. Simulation results

indicate that the proposed method effectively detects and

recognizes zebra crossing regions from various zebra-crossing

images. Moreover, it shows superior performance than the stateof-

the art methods in terms of recognition