Distance Detection Technique Using Enhancing Inverse Perspective Mapping

Wongsaree, Preaw; Sinchai, Sakkarin; Wardkein, P.; Koseeyaporn, J.

doi:10.1109/ccoms.2018.8463318

Cited by 13 publications

(8 citation statements)

References 3 publications

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“…Moreover, IPM can also be used to obtain more information about the vehicle to be detected. In [ 110 ], ROIs were extracted based on IPM and the distance between the vehicle and the camera center was obtained, while in [ 111 ], IPM was combined with CNN to obtain the location, size and attitude angle of vehicles.…”

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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“…The second category involves constructing a regression model using machine learning. Wongsaree et al trained a regression model using the correspondence between different positions in an image and their corresponding distances to complete distance estimation [13]. Gökçe et al used the target vehicle information to train a distance regression model for distance estimation [14].…”

Section: Related Workmentioning

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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“…In accordance with different projection principles, distance estimation methods can be further divided into the inverse perspective mapping (IPM) principle and the camera projection principle. In target vehicle distance estimation based on the IPM principle [1], [3], [4], the original image is converted into a bird s eye view via IPM transformation to restore the information of the road plane. Then, the distance of the target vehicle is calculated using the IPM image obtained after the conversion.…”

Section: Stereo Vision Can More Intuitively and Accurately Calculatementioning

confidence: 99%

“…In recent years, research on the methods for estimating the distance of monocular vision vehicles based on geometric models, which are largely divided into the inverse perspective mapping transformation method [3], projection geometric relationship method [5], and fitting modeling method [16], has achieved considerable results. In [4], [17], [18], [20], a distance estimation method based on IPM was proposed. The difference among these studies is the object detection method, such as the road removal algorithm [4], [17], threshold adjustment [18], and hue, saturation, and value (HSV) color mapping [20].…”

Section: Related Workmentioning

confidence: 99%

“…In [4], [17], [18], [20], a distance estimation method based on IPM was proposed. The difference among these studies is the object detection method, such as the road removal algorithm [4], [17], threshold adjustment [18], and hue, saturation, and value (HSV) color mapping [20]. The overall idea is to convert the original image into a bird s eye view, which approximates the image obtained from the top-down observation scene.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision

Huang

Zhe

et al. 2019

IEEE Access

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Advanced driver assistance systems (ADAS) based on monocular vision are rapidly becoming a popular research subject. In ADAS, inter-vehicle distance estimation from an in-car camera based on monocular vision is critical. At present, related methods based on a monocular vision for measuring the absolute distance of vehicles ahead experience accuracy problems in terms of the ranging result, which is low, and the deviation of the ranging result between different types of vehicles, which is large and easily affected by a change in the attitude angle. To improve the robustness of a distance estimation system, an improved method for estimating the distance of a monocular vision vehicle based on the detection and segmentation of the target vehicle is proposed in this paper to address the vehicle attitude angle problem. The angle regression model (ARN) is used to obtain the attitude angle information of the target vehicle. The dimension estimation network determines the actual dimensions of the target vehicle. Then, a 2D base vector geometric model is designed in accordance with the image analytic geometric principle to accurately recover the back area of the target vehicle. Lastly, area-distance modeling based on the principle of camera projection is performed to estimate distance. The experimental results on the real-world computer vision benchmark, KITTI, indicate that our approach achieves superior performance compared with other existing published methods for different types of vehicles (including front and sideway vehicles).

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Distance Detection Technique Using Enhancing Inverse Perspective Mapping

Cited by 13 publications

References 3 publications

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Vision-Based Distance Measurement in Advanced Driving Assistance Systems

Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision

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