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
