A Texture-Based Local Soft Voting Method for Vanishing Point Detection from a Single Road Image

• Published in: IEICE Transactions on Information and Systems Vol. E96.D; no. 3; pp. 690 - 698
• Main Authors: BUI, Trung Hieu, NOBUYAMA, Eitaku, SAITOH, Takeshi
• Format: Journal Article
• Language: English
• Published: Oxford The Institute of Electronics, Information and Communication Engineers 2013; Oxford University Press
• Subjects: Applied sciences; Artificial intelligence; Circuit properties; Computational efficiency; Computer science; control theory; systems; Detection, estimation, filtering, equalization, prediction; Electric, optical and optoelectronic circuits; Electronic circuits; Electronics; Exact sciences and technology; Frequency filters; Gabor filter; Image detection; Information, signal and communications theory; Pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Pixels; Roads; Signal and communications theory; Signal processing; Signal, noise; soft voting; Surface layer; Telecommunications and information theory; Texture; texture-based; vanishing point; Voting; Estimation error; Gabor filter; vanishing point; Gabor transformation; Pattern recognition; soft voting; Shape detection; Computational complexity; Adaptive method; Texture; Implementation; texture-based; Accuracy; Algorithm performance; Wavelet transformation; Voting; Signal processing; Filter bank; Localization; Focus of expansion; Edge detection
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.E96.D.690

Estimating a proper location of vanishing point from a single road image without any prior known camera parameters is a challenging problem due to limited information from the input image. Most edge-based methods for vanishing point detection only work well for structured roads with clear painted lines or distinct boundaries, while they usually fail in unstructured roads lacking sharply defined, smoothly curving edges. In order to overcome this limitation, texture-based methods for vanishing point detection have been widely published. Authors of these methods often calculate the texture orientation at every pixel of the road image by using directional filter banks such as Gabor wavelet filter, and seek the vanishing point by a voting scheme. A local adaptive soft voting method for obtaining the vanishing point was proposed in a previous study. Although this method is more effective and faster than prior texture-based methods, the associated computational cost is still high due to a large number of scanning pixels. On the other hand, this method leads to an estimation error in some images, in which the radius of the proposed half-disk voting region is not large enough. The goal of this paper is to reduce the computational cost and improve the performance of the algorithm. Therefore, we propose a novel local soft voting method, in which the number of scanning pixels is much reduced, and a new vanishing point candidate region is introduced to improve the estimation accuracy. The proposed method has been implemented and tested on 1000 road images which contain large variations in color, texture, lighting condition and surrounding environment. The experimental results demonstrate that this new voting method is both efficient and effective in detecting the vanishing point from a single road image and requires much less computational cost when compared to the previous voting method.