A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes

Khac, Cuong Nguyen; Choi, Yang‐Kyu; Park, Ju H.; Jung, Ho-Youl

doi:10.3390/s21062133

Cited by 8 publications

(5 citation statements)

References 48 publications

(87 reference statements)

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“…The LCS method proposed in this paper is compared with 6 existing vanishing point detection methods of different types. They are the edge-based method 42 , motion-based Road vanishing point detection R-VP 43 , as well as the classic methods of Kong (Gabor) 44 and yang 45 , the deep learning-based CNN method HrNet 12 , and the disappearance of MST clustering point detection method Hwang 47 .…”

Section: Methodsmentioning

confidence: 99%

A fast vanishing point detection method based on row space features suitable for real driving scenarios

Qin

et al. 2023

Sci Rep

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The vanishing point (VP) is particularly important road information, which provides an important judgment criterion for the autonomous driving system. Existing vanishing point detection methods lack speed and accuracy when dealing with real road environments. This paper proposes a fast vanishing point detection method based on row space features. By analyzing the row space features, clustering candidates for similar vanishing points in the row space are performed, and then motion vectors are screened for the vanishing points in the candidate lines. The experimental results show that the average error of the normalized Euclidean distance is 0.0023716 in driving scenes under various lighting conditions. The unique candidate row space greatly reduces the amount of calculation, making the real-time FPS up to 86. It can be concluded that the fast vanishing point detection proposed in this paper would be suitable for high-speed driving scenarios.

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Section: Methodsmentioning

confidence: 99%

A fast vanishing point detection method based on row space features suitable for real driving scenarios

Qin

et al. 2023

Sci Rep

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“…In 2021, Khac et al proposed a robust motion-based road VP detection method (R-VP detection method) for real-world driving scenes. In the study, the proposed method that consists of stable motion detection, stationary point-based motion vector selection, and angle-based RANSAC voting achieves high accuracy and robustness in the Jiqing Expressway dataset [33]. In 2018, an accurate and efficient road vanishing point detection scheme based on the v-disparity and visual odometry techniques was proposed.…”

Section: Vanishing Pointmentioning

confidence: 99%

An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments

Chai

Zhang

et al. 2021

Sensors

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The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.

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“…They employed an adaptive soft voting approach to estimate a vanishing point and represent the texture orientation's confidence rating. Khac et al [13] proposed a technique for evaluating the efficacy of lane identification on both structured and unstructured roadways based on a vanishing point estimate. One of these methodologies' significant flaws is that when the vanishing point's position changes, the vanishing point cannot be accurately predicted.…”

Section: Introductionmentioning

confidence: 99%

A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

Noman

Zhai²,

Almukhtar³

et al. 2023

IJECE

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<span lang="EN-US">Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.</span>

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A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes

Cited by 8 publications

References 48 publications

A fast vanishing point detection method based on row space features suitable for real driving scenarios

A fast vanishing point detection method based on row space features suitable for real driving scenarios

An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments

A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

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