Robust vanishing point detection based on block wise weighted soft voting scheme

Abstract

Vanishing point detection is a challenging task due to the variations in road types and its cluttered background. Currently, most existing texture-based methods detect the vanishing point using pixel-wise voting map generation, which suffers from high computational complexity and the noise votes introduced by the incorrectly estimated texture orientations. In this paper, a block wise weighted soft voting scheme is developed for good performance in complex road scenes. First, the gLoG filters are applied to estimate the texture orientation of each pixel. Then, the image is divided into blocks in a sliding fashion, and a histogram is constructed based on the texture orientation of pixels within each block to obtain the dominant orientation bin. Instead of using the texture orientation of all valid pixels within each block, only the dominant orientation bin is utilized to perform a weighted soft voting. The experimental results on the benchmark dataset show that the proposed method achieves the best performance among all, when compared with the state-of-the-art works.