Camera orientation estimation using motion based vanishing point detection for automatic driving assistance system

Jo, Youngran; Jang, Jinbeum; Paik, Joonki

doi:10.1109/icce.2018.8326303

Cited by 6 publications

(8 citation statements)

References 2 publications

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“…In the motion-based R-VP detection methods [ 34 , 35 , 36 , 37 ], motion vectors are used as line segments. Most existing motion-based methods detect a fixed number of corner points in a frame by using corner detectors such as the Shi-Tomasi corner detector [ 54 ].…”

Section: Related Workmentioning

confidence: 99%

“…However, in the point of view of the R-VP detection problem, these motion vectors still cause some errors for R-VP detection. In detail, most existing motion-based R-VP detection methods assume that longer motion vectors are more convergent to R-VP locations [ 34 , 35 , 36 , 37 ]. However, it can be seen in Figure 2 a that the short vectors also coincide on the lanes and road boundaries.…”

Section: Related Workmentioning

confidence: 99%

“…Any point in the ini- The existing motion-based methods attempt to select useful vectors. Motion vectors that are long and not belonging to a certain area where other moving objects exist are selected [34][35][36][37]. In the methods, they eliminate the vectors in the area of on-coming and in-parallel driving vehicles.…”

Section: Stable Motion Vector Detectionmentioning

confidence: 99%

“…Recently, the number of geometrical feature-based methods for R-VP detection increased significantly. These recent existing methods can be classified into four groups based on types of geometrical features used in the methods, such as line segment-based [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], edge-based [ 30 , 31 , 32 , 33 ], motion-based [ 34 , 35 , 36 , 37 ], and texture-based methods [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the number of geometrical feature-based methods for R-VP detection increased significantly. These recent existing methods can be classified into four groups based on types of geometrical features used in the methods, such as line segment-based , edge-based [30][31][32][33], motion-based [34][35][36][37], and texture-based methods [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

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

Khac

Choi

Park

et al. 2021

Sensors

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Vanishing point (VP) provides extremely useful information related to roads in driving scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Existing VP detection methods for driving scenes still have not achieved sufficiently high accuracy and robustness to apply for real-world driving scenes. This paper proposes a robust motion-based road VP detection method to compensate for the deficiencies. For such purposes, three main processing steps often used in the existing road VP detection methods are carefully examined. Based on the analysis, stable motion detection, stationary point-based motion vector selection, and angle-based RANSAC (RANdom SAmple Consensus) voting are proposed. A ground-truth driving dataset including various objects and illuminations is used to verify the robustness and real-time capability of the proposed method. The experimental results show that the proposed method outperforms the existing motion-based and edge-based road VP detection methods for various illumination conditioned driving scenes.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Stable Motion Vector Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Recently, the number of geometrical feature-based methods for R-VP detection increased significantly. These recent existing methods can be classified into four groups based on types of geometrical features used in the methods, such as line segment-based , edge-based [30][31][32][33], motion-based [34][35][36][37], and texture-based methods [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

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

Khac

Choi

Park

et al. 2021

Sensors

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Guided Feature Selection for Deep Visual Odometry

Xue

Wang

et al. 2019

Lecture Notes in Computer Science

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We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks. Different from current monocular visual odometry methods, our approach is established on the intuition that features contribute discriminately to different motion patterns. Specifically, we propose a dual-branch recurrent network to learn the rotation and translation separately by leveraging current Convolutional Neural Network (CNN) for feature representation and Recurrent Neural Network (RNN) for image sequence reasoning. To enhance the ability of feature selection, we further introduce an effective context-aware guidance mechanism to force each branch to distill related information for specific motion pattern explicitly. Experiments demonstrate that on the prevalent KITTI and ICL NUIM benchmarks, our method outperforms current state-of-theart model-and learning-based methods for both decoupled and joint camera pose recovery.

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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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Camera orientation estimation using motion based vanishing point detection for automatic driving assistance system

Cited by 6 publications

References 2 publications

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

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

Guided Feature Selection for Deep Visual Odometry

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

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