Zebra-crossing detection on road images using dynamic time warping
This work considers zebra crossing detection problem for autonomous ground vehicle self-localization. The proposed algorithm accepts undistorted bird's-eye view image as an input. The input image is transformed in the way that road lanes are parallel to the image columns. In case of successful crossing detection, the algorithm outputs the upper and the lower crossing borders. Image rows are classified as belonging to pedestrian crossing based on the quasi-periodical alternation of contrast edges using the dynamic programming approach. The long edge orientation of row group is checked with the use of Fast Hough transform. The proposed method has been validated on the dataset of 4477 images collected from the real vehicle on two test routes. The presented experimental results demonstrate a decrease in the average localization error by 1.78 times.