Vanishing Point Candidates Research Articles

역진자 주행로봇의 피치 모션에 강인한 경계추종 자율주행 기법

In a vision-based autonomous driving of a mobile robot, heavy pitch motions can significantly deteriorate the boundary, or lane-following, performance. Such motions can result from the high maneuvers of an inverted pendulum balancing robot. If the vision camera frequently tilts forward or backward according to the mobile platform’s pitch motion, it would fail to extract drivable regions from the image data. It would also fail to detect successive lanes or boundaries to determine local paths, which is mainly due to the vision camera’s extrinsic parameter variation. In this paper, we investigate a local path planning method that accounts for the heavy pitch motion of a mobile robot by considering the camera posture in real-time image processing. Fundamentally, this method is based on the vanishing point tracking and the pinhole camera model. The highlight of this paper lies in employing the vertical filter to exclude vertical outliers from the boundary data and also the image horizon filter to determine the valid vanishing point candidates. Indoor and outdoor experiments demonstrate the effectiveness of the proposed method.

Optimized Clustering Scheme-Based Robust Vanishing Point Detection

Robust vanishing point estimation has been widely applied to various applications in the field of computer vision and pattern recognition for robotics, advanced driver assistance systems, and autonomous driving vehicles. The major challenge for vanishing point detection lies in line segments, spurious vanishing candidate removal, and clustering for refinement. Recent vanishing point detection approaches have attempted to reduce the computational complexity involved with voting processes using optimized voter selection strategies to identify the vanishing point from line segments. This paper proposes a novel vanishing point detection method to select robust candidates, applying optimized minimum spanning tree-based clustering of the vanishing point candidates by analyzing the lines within a unit sphere domain. The proposed scheme was applied to an open database that included illumination, partial occlusion, and viewpoint changes to validate robustness without prior scene information.

소실점 추정 정확도 개선을 통한 SVM 성능 향상

In this paper, we propose an improved single view metrology (SVM) algorithm to accurately measure the height of objects. In order to accurately measure the size of objects, vanishing points have to be correctly estimated. There are two methods to estimate vanishing points. First, the user has to choose some horizontal and vertical lines in real world. Then, the user finds the cross points of the lines. Second, the user can obtain the vanishing points by using software algorithm such as [6-9]. In the former method, the user has to choose the lines manually to obtain accurate vanishing points. On the other hand, the latter method uses software algorithm to automatically obtain vanishing points. In this paper, we apply image resizing and edge sharpening as a pre-processing to the algorithm in order to improve performance. The estimated vanishing points algorithm create four vanishing point candidates: two points are horizontal candidates and the other two points are vertical candidates. However, a common image has two horizontal vanishing points and one vertical vanishing point. Thus, we eliminate a vertical vanishing point candidate by analyzing the histogram of angle distribution of vanishing point candidates. Experimental results show that the proposed algorithm outperforms conventional methods, [6] and [7]. In addition, the algorithm obtains similar performance with manual method with less than 5% of the measurement error.

Fast and Robust Vanishing Point Detection for Unstructured Road Following

Vision-based unstructured road following is a challenging task due to the nature of the scene. This paper describes a novel algorithm to improve the accuracy and robustness of vanishing point estimation with very low computational cost. The novelties of this paper are three aspects: 1) We use joint activities of four Gabor filters and confidence measure for speeding up the process of texture orientation estimation. 2) Misidentification chances and computational complexity of the algorithm are reduced by using a particle filter. It limits vanishing point search range and reduces the number of pixels to be voted. The algorithm combines the peakedness measure of vote accumulator space with the displacements of moving average of observations to regulate the distribution of vanishing point candidates. 3) Attributed to the design of a noise-insensitive observation model, the proposed system still has high detection accuracy even when less than 60 sparsely distributed vanishing point candidates are used for voting as the influence introduced by the stochastic measurement noise of vote function and the sparsity of the vanishing point candidates is reduced. The method has been implemented and tested over 20 000 video frames. Experimental results demonstrate that the algorithm achieves better performance than some state-of-the-art texture-based vanishing point detection methods in terms of detection accuracy and speed.

Efficient vanishing point detection method in complex urban road environments

Detecting the vanishing point in a road image is important for robot navigation, intelligent transportation and other fields. This study proposes a new vanishing point detection method that uses the vertical information in complex urban road and street environments. First, all straight lines in the road image are detected based on curvature scale space and principal component analysis methods. Second, a new road region extraction method is proposed to support vanishing point detection. In this method, the image is decomposed into approximate sky, vertical and road regions using the estimation envelopes of the vertical lines. Third, the straight lines in the road region are extracted using a path perspective triangle and line length limits. The straight lines are then categorised into groups using proposed grouping strategies. Finally, vanishing point candidates are calculated from paired lines extracted from different groups and from the same group, and the final vanishing point is then estimated for the urban road scene using the mean shift clustering method. The experimental results show that the proposed algorithm can estimate the vanishing point accurately and efficiently in complex urban road environments, despite interference from vehicles and pedestrians and on curved and unstructured roads.