Fast Vanishing-Point Detection in Unstructured Environments

Abstract

Vision-based road detection in unstructured environments is a challenging problem as there are hardly any discernible and invariant features that can characterize the road or its boundaries in such environments. However, a salient and consistent feature of most roads or tracks regardless of type of the environments is that their edges, boundaries, and even ruts and tire tracks left by previous vehicles on the path appear to converge into a single point known as the vanishing point. Hence, estimating this vanishing point plays a pivotal role in the determination of the direction of the road. In this paper, we propose a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads. The key attributes of the methodology consist of the optimal local dominant orientation method that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane, the weighting of each pixel based on its dominant orientation, and an adaptive distance-based voting scheme for the estimation of the vanishing point. A series of quantitative and qualitative analyses are presented using natural data sets from the Defense Advanced Research Projects Agency Grand Challenge projects to demonstrate the effectiveness and the accuracy of the proposed methodology.

Real-Time Road-Direction Point Detection in Complex Environment

“…In order to compare our method with traditional texturebased vanishing point detection methods, we use normalized Euclid distance error (NormDist Error) as introduced in [12] to evaluate performance. This distance error is defined as the Euclid distance between the estimated and ground truth vanishing point positions divided by the diagonal length of the whole image.…”

“…[11] proposed a scheme of adaptive soft voting to detect vanishing point through confidence weighted Gabor kernels. [12] applied only four directions of Gabor filters to estimate dominant orientations which can speed up convolutional processing. [13] developed a model by combining binaryapproximated Gabor filters and a cascaded voting scheme to reduce computational complexity for vanishing point prediction with less time cost.…”

Fast Vanishing Point Estimation Based on Particle Swarm Optimization

“…Different methods were proposed for estimating texture orientation. C. Russmusen, Kong, Moghadam et al [4], [5], [15] filter banks to estimate it [14]; and Miksik, Wang et al used Haar-like box to do it [8], [11]. Here we mainly follow the Gabor filter based method.…”

“…Moghadam et al used skyline to reduce the candidates of vanishing point [10]. Furthermore, Haar functions, integral image [9] and orthogonal Gabor [4] filters are also used to improve the efficiency of the vanishing point estimation [9]. This paper also focuses on improving the efficiency of the conventional vanishing point estimation based on C. Russmusen and Kong's method.…”

Optimized Voting Scheme for Efficient Vanishing Point Detection in General Road Images

“…al. [18] proposed a novel methodology based on image texture analysis for the fast estimation of the vanishing point detection in the challenging environments. The key attributes of the methodology consist of the optimal local dominant orientation method that uses joint activity of four Gabor filters followed by an efficient and robust voting scheme for real-time detection of the vanishing point.…”