Inter-Vehicle Distance Estimation Method Based on Monocular Vision Using 3D Detection

The camera calibration in monocular vision represents the relationship between the pixels’ units which is obtained from a camera and the object in the real world. As an essential procedure, camera calibration calculates the three-dimensional geometric information from the captured two-dimensional images. Therefore, a modified camera calibration method based on polynomial regression is proposed to simplify. In this method, a parameter vector is obtained by pixel coordinates of obstacles and corresponding distance values using polynomial regression. The set of parameter’s vectors can measure the distance between the camera and the ground object in the field of vision under the camera’s posture and position. The experimental results show that the lowest accuracy of this focal length calibration method for measurement is 97.09%, and the average accuracy was 99.02%.

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“…Ranging accuracy Bao's [5] 97% Meng's [6] More than 90% Zhe's [24] Approximate 98% Proposed Average 97.09%, maximum 99.02%…”

Section: Methodsmentioning

confidence: 99%

Monocular Vision Ranging and Camera Focal Length Calibration

Liu

Fan

et al. 2021

Scientific Programming

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“…This end-to-end learning is optimized by minimizing the difference between estimation and ground truth range. Unlike previous end-to-end object-specific distance estimation methods [ 13 , 14 , 15 ] that require expensive object-level annotations as supervision, our method is only trained with ground truth range and has no restriction on object category, which makes it possible to generalize to objects of unseen categories. Meanwhile, we have no direct supervision on the predicted weight map.…”

Section: Approachmentioning

confidence: 99%

“…Recently, deep learning methods have achieved great success in the computer vision community. For range estimation, prior works [ 13 , 14 , 15 ] have proposed object-specific end-to-end deep learning frameworks. They follow the multi-task learning scheme to simultaneously detect objects and estimate the corresponding range for each object by direct regression.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Monocular Range Estimation for Forward Collision Warning

Tang

2020

Sensors

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Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specific categories relying on expensive object-level annotation for training, but not for unseen categories. In this paper, we present an end-to-end deep learning architecture to solve the above problems. Specifically, we represent the target range as a weighted sum of a set of potential distances. These potential distances are generated by inverse perspective projection based on intrinsic and extrinsic camera parameters, while a deep neural network predicts the corresponding weights of these distances. The whole architecture is optimized towards the range estimation task directly in an end-to-end manner with only the target range as supervision. As object category is not restricted in the training stage, the proposed method can generalize to objects with unseen categories. Furthermore, camera parameters are explicitly considered in the proposed method, making it able to generalize to images taken with different cameras and novel views. Additionally, the proposed method is not a pure black box, but provides partial interpretability by visualizing the produced weights to see which part of the image dominates the final result. We conduct experiments to verify the above properties of the proposed method on synthetic and real-world collected data.

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“…where the threshold usually takes three values: 1.25, 1.25 2 , and 1.25 3 ; for different thresholds, there are different threshold accuracies: δ < 1.25, δ < 1.25 2 , and δ < 1.25 3 ; N is the number of pixels with ground truth in the test set; z i and z i are the predicted depth value and true depth value, respectively. Regarding the above evaluation indexes, the smaller the first two parameters (AbsRel and RMSE), the higher the accuracy of the depth estimation result.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…As one of the key technologies, Advanced Driving Assistance Systems (ADASs) are developing rapidly [1]. Measuring the vehicle-vehicle and vehicle-pedestrian distance is one of the main tasks of 2 of 19 ADASs [2,3]. Generally speaking, existing measurement methods can be placed into two categories [4]: active sensor-based methods and passive vision-based methods.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Distance Measurement in Advanced Driving Assistance Systems

et al. 2020

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As the forward-looking depth information plays a considerable role in advanced driving assistance systems, in this paper, we first propose a method of depth map estimation based on semi-supervised learning, which uses the left and right views of binocular vision and sparse depth values as inputs to train a deep learning network with an encoding–decoding structure. Compared with unsupervised networks without sparse depth labels, the proposed semi-supervised network improves the estimation accuracy of depth maps. Secondly, this paper combines the estimated depth map with the results of instance segmentation to measure the distance between the subject vehicle and the target vehicle or pedestrian. Specifically, for measuring the distance between the subject vehicle and a pedestrian, this paper proposes a depth histogram-based method that calculates the average depth values of all pixels whose depth values are in the peak range of the depth histogram of this pedestrian. To measure the distance between the subject vehicle and the target vehicle, this paper proposes a method that first fits a 3-D plane based on the locations of target points in the camera body coordinate using RANSAC (RANdom SAmple Consensus), it then projects all the pixels of the target to this plane, and finally uses the minimum depth value of these projected points to calculate the distance to the target vehicle. The results of the quantitative and qualitative comparisons on the KITTI dataset show that the proposed method can effectively estimate depth maps. The experimental results in real road scenarios and the KITTI dataset confirm the accuracy of the proposed distance measurement methods.

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Inter-Vehicle Distance Estimation Method Based on Monocular Vision Using 3D Detection

Cited by 50 publications

References 36 publications

Monocular Vision Ranging and Camera Focal Length Calibration

Monocular Vision Ranging and Camera Focal Length Calibration

End-to-End Monocular Range Estimation for Forward Collision Warning

Vision-Based Distance Measurement in Advanced Driving Assistance Systems

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