Zebra crossing detection based on HSV colour model and projective invariant for driver assistance system

This paper implements a colour and shape-based Zebra crossings detection model. As Zebra crossings are white coloured, a robust binarisation based on the V channel of the input image is used to find the large rectangle sized white stripes of Zebra crossing. After the morphological open operation, boundary tracing is done using Moore-neighbourhood tracing algorithm along with Jacob's stopping criterion. For every region, the bounding quadrilateral with the minimum area is computed and the candidate regions are then filtered out using some criteria, e.g., aspect ratio. The y-coordinates of the lower vertices of the white stripes are considered as 'cluster' and the optimal number of clusters is determined using the Calinski-Harabasz criterion. These clusters are now checked to determine whether they represent crosswalks and then validate using projective invariant. Various Zebra crossing images are used to test the proposed framework and the results are presented to prove its effectiveness.