Vanishing point estimation for challenging road images

She, Qingyun; Lu, Zongqing; Liao, Qingmin

doi:10.1109/icip.2014.7025200

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…In sum, experimental results show that the overall performance of the method in this paper can be in real time and performs significantly better than the other two method, the edge-based VP detection [24] and the texture-based VP detection [23], which indicates that the proposed method can achieve good performance in terms of both the effectiveness and the efficiency.…”

Section: Results and Analysis A Experiments About Vanishing Point mentioning

confidence: 87%

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An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

Zhu

2020

IEEE Access

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Simultaneous Localization and Mapping (SLAM) is the process by which a mobile robot carrying specific sensors builds a map of the environment and at the same time uses this map to estimate its pose. Currently, SLAM has been proven its value and is a hot topic. However, challenges still exist: when the mobile robot stops in the process of motion and a large number of feature points in the environment move slightly, the feature matching process cannot eliminate the non-stationary feature point pairs. The introduction of a large number of outliers (non-stationary feature point pairs) seriously affects the observation process of SLAM. It directly leads to the estimation errors of mobile robot pose and the 3D features position, and further leads to keyframe trajectory drift. If there is a mechanism that allows the robot to accurately detect itself in a stop state, then the pose and map points could be locked, and the state variables could be optimized to make the system enter the positive succession. Therefore, detecting the stop status of the mobile robot is a significant work for SLAM. In this manuscript, an improved phase correlation method is proposed to solve the problem of stop detection for the autonomous driving vehicle in the dynamic street environment. After experiments, it is revealed that the stop detection is significant for the performance improvement to the state-of-the-art visual SLAM system, and the improved phase correlation method has higher stop detection accuracy than the conventional phase correlation in various scenarios. INDEX TERMS Stop detection, phase correlation, simultaneous localization and mapping, vanishing point.

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Section: Results and Analysis A Experiments About Vanishing Point mentioning

confidence: 87%

“…Obviously, the smaller the NormDist, the higher the accuracy of the detected result [39]. For analyzing the performance of the algorithm proposed in this paper, we compare it with the edge-based VP detection algorithm [24] and the texture-based VP detection algorithm [23]. Fig.…”

Section: Results and Analysis A Experiments About Vanishing Point mentioning

confidence: 99%

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

Zhu

2020

IEEE Access

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“…Nevertheless, this method entails a high computing cost and poor timeliness. Currently, the Hough transform, which employs an edge-based algorithm, is a method that shows a relatively excellent performance ( Liu and Zhou, 2015 ; She et al, 2015 ). It extracts straight lines in an image using an edge detector for the Hough transform and uses voting to locate vanishing points.…”

Section: Introductionmentioning

confidence: 99%

Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads

Gong

Liu

2022

Front. Bioeng. Biotechnol.

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To solve the problems of computational complexity and inaccuracy in classical vanishing point detection algorithms, such as the cascaded Hough transform, a vanishing point detection method based on constrained classification is proposed. First, the short line data are filtered to avoid interference in straight line detection, and then, the line segment is screened and classified by hierarchical clustering according to the image characteristics of the line segment and the variation pattern of angle similarity. Subsequently, Three types of straight line segments with the most significant angle differences are acquired. To prevent the optimization algorithm from getting stuck in the “wrong” local optimum neighborhood or failing to locate the global optimum, a set of constraints are set to further restrict the search. Afterward, the classified line segments are projected into a finite rhombic space, which are then quantified. The point with the maximum vote is eventually identified as the vanishing point. Experimental results show that the proposed method not only greatly reduces the computational complexity of vanishing points but also largely improves the accuracy of vanishing point detection.

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“…Although the three methods mentioned above efficiently obtain vanishing point location in many road scenes, when the background of the unstructured road scenes is too complex, the noise lines from the complex background that affect the accuracy of vanishing point detection will be introduced, which lead to the failure of these methods. To address this challenge, some existing methods (She et al, 2014;Ding et al, 2016;Li et al, 2016) try to remove part of the background region. She et al (2014) believe that the top region of the road image is always the sky, which makes no contribution to the vanishing point detection.…”

Section: Introductionmentioning

confidence: 99%

“…To address this challenge, some existing methods (She et al, 2014;Ding et al, 2016;Li et al, 2016) try to remove part of the background region. She et al (2014) believe that the top region of the road image is always the sky, which makes no contribution to the vanishing point detection. Therefore, they remove the sky region through a brightness-enhanced based method.…”

Section: Introductionmentioning

confidence: 99%

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

Han

Yang

et al. 2019

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Purpose This paper aims to propose a robust and efficient method for vanishing point detection in unstructured road scenes. Design/methodology/approach The proposed method includes two main stages: drivable region estimation and vanishing point detection. In drivable region estimation stage, the road image is segmented into a set of patches; then the drivable region is estimated by the patch-wise manifold ranking. In vanishing point detection stage, the LSD method is used to extract the straight lines; then a series of principles are proposed to remove the noise lines. Finally, the vanishing point is detected by a novel voting strategy. Findings The proposed method is validated on various unstructured road images collected from the real world. It is more robust and more efficient than the state-of-the-art method and the other three recent methods. Experimental results demonstrate that the detected vanishing point is practical for vision-sensor-based navigation in complex unstructured road scenes. Originality/value This paper proposes a patch-wise manifold ranking method to estimate the drivable region that contains most of the informative clues for vanishing point detection. Based on the removal of the noise lines through a series of principles, a novel voting strategy is proposed to detect the vanishing point.

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Vanishing point estimation for challenging road images

Cited by 9 publications

References 11 publications

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

Vanishing Point Detection Method Based on Constrained Classification for Checkpoints on Urban Roads

Robust and efficient vanishing point detection in unstructured road scenes for assistive navigation

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