scholarly journals A Taillight Matching and Pairing Algorithm for Stereo-Vision-Based Nighttime Vehicle-to-Vehicle Positioning

2020 ◽  
Vol 10 (19) ◽  
pp. 6800
Author(s):  
Thai-Hoa Huynh ◽  
Myungsik Yoo
Keyword(s):  
Stereo Vision ◽  
Autonomous Vehicles ◽  
Stereo Matching ◽  
Vision System ◽  
Urban Traffic ◽  
Vehicle To Vehicle ◽  
Potential Benefits ◽  
Vehicle Positioning ◽  
Left Image ◽  
The Right

The stereo vision system has several potential benefits for delivering advanced autonomous vehicles compared to other existing technologies, such as vehicle-to-vehicle (V2V) positioning. This paper explores a stereo-vision-based nighttime V2V positioning process by detecting vehicle taillights. To address the crucial problems when applying this process to urban traffic, we propose a three-fold contribution as follows. The first contribution is a detection method that aims to label and determine the pixel coordinates of every taillight region from the images. Second, a stereo matching method derived from a gradient boosted tree is proposed to determine which taillight in the left image a taillight in the right image corresponds to. Third, we offer a neural-network-based method to pair every two taillights that belong to the same vehicle. The experiment on the four-lane traffic road was conducted, and the results were used to quantitatively evaluate the performance of each proposed method in real situations.

PRECISE SHAPE MEASUREMENT OF DYNAMIC SURFACE VIA SINGLE CAMERA STEREO

2006 ◽  
Vol 03 (01) ◽  
pp. 53-60
Author(s):  
LUPING AN ◽  
YUNDE JIA ◽  
MINGTAO PEI ◽  
HONGBIN DENG
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Vision System ◽  
Least Square ◽  
Shape Measurement ◽  
Single Camera ◽  
Dynamic Surface ◽  
Moving Least Square ◽  
Stereo Vision System ◽  
Precise Shape

In this article, a method of the precise shape measurement of dynamic surfaces via a single camera stereo vision system is presented, a cross-curve pattern is painted on the surface of an object, and the intersections of cross-curves which represent the shape of the object are measured by the stereo vision system. The system with a single camera is modeled as a virtual binocular stereo by strong calibration technique. Binocular epipolar rectification is used to make the stereo matching efficient, and principal curves theory is employed to extract curves in images for stereo matching. Under the framework of RANSAC, the curves are interpolated robustly with cubic spline based on moving-least-square (MLS). Experimental results on both static and dynamic deforming surfaces illustrate the effectiveness of the proposed method.

scholarly journals Three-dimensional mechanical parts reconstruction technology based on two-dimensional image

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091000
Author(s):  
Jiaofei Huo ◽  
Xiaomo Yu
Keyword(s):  
Image Processing ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Vision System ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Stereo Vision System ◽  
Binocular Stereo Vision ◽  
Extraction Algorithm ◽  
Dimensional Reconstruction

With the development of computer technology and three-dimensional reconstruction technology, three-dimensional reconstruction based on visual images has become one of the research hotspots in computer graphics. Three-dimensional reconstruction based on visual image can be divided into three-dimensional reconstruction based on single photo and video. As an indirect three-dimensional modeling technology, this method is widely used in the fields of film and television production, cultural relics restoration, mechanical manufacturing, and medical health. This article studies and designs a stereo vision system based on two-dimensional image modeling technology. The system can be divided into image processing, camera calibration, stereo matching, three-dimensional point reconstruction, and model reconstruction. In the part of image processing, common image processing methods, feature point extraction algorithm, and edge extraction algorithm are studied. On this basis, interactive local corner extraction algorithm and interactive local edge detection algorithm are proposed. It is found that the Harris algorithm can effectively remove the features of less information and easy to generate clustering phenomenon. At the same time, the method of limit constraints is used to match the feature points extracted from the image. This method has high matching accuracy and short time. The experimental research has achieved good matching results. Using the platform of binocular stereo vision system, each step in the process of three-dimensional reconstruction has achieved high accuracy, thus achieving the three-dimensional reconstruction of the target object. Finally, based on the research of three-dimensional reconstruction of mechanical parts and the designed binocular stereo vision system platform, the experimental results of edge detection, camera calibration, stereo matching, and three-dimensional model reconstruction in the process of three-dimensional reconstruction are obtained, and the full text is summarized, analyzed, and prospected.

High-Precision 3D Modeling Method Based on Terrestrial Image Sequences for Alpine-Gorge Area

2012 ◽  
Vol 182-183 ◽  
pp. 1270-1275 ◽  
Author(s):  
Bo Su ◽  
Hao Li ◽  
Ya Qin Wang ◽  
Biao Yang
Keyword(s):  
Stereo Matching ◽  
Three Dimensional ◽  
Digital Camera ◽  
Image Sequences ◽  
Three Dimensional Modeling ◽  
Modeling Process ◽  
Dimensional Modeling ◽  
Left Image ◽  
The Right ◽  
Seed Points

The traditional measurement methods cannot adapt to the arduous topography of alpine-gorge area. Aiming at the topographical features of alpine-gorge area, we will introduce a general terrestrial method of multi-baseline photogrammetry basing on digital camera here, and then the paper mainly studies the metrization method of common digital camera and matching method of the digital image sequences of alpine-gorge area. Through the metrization of common digital camera, the efficiency of terrain data collection will increase in the alpine-gorge area, and the requirements of operations on the image control and algorithm will reduce. The combination of seed points and multiple constraints in multi-baseline stereo matching will help to solve many problems, such as shading, severe distortion between the left image and the right one, and the inconformity of scale. The modeling process stated above is quite fast and highly precise, and the three-dimensional modeling experiments show that the relative accuracy can reach from 1 / 8000 to 1 / 12000.

scholarly journals A Unified Framework for Depth Prediction from a Single Image and Binocular Stereo Matching

2020 ◽  
Vol 12 (3) ◽  
pp. 588
Author(s):  
Wei Chen ◽  
Xin Luo ◽  
Zhengfa Liang ◽  
Chen Li ◽  
Mingfei Wu ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Depth Information ◽  
Training Procedure ◽  
Single Image ◽  
Unified Framework ◽  
Depth Prediction ◽  
Training Samples ◽  
Binocular Stereo ◽  
Left Image ◽  
The Right

Depth information has long been an important issue in computer vision. The methods for this can be categorized into (1) depth prediction from a single image and (2) binocular stereo matching. However, these two methods are generally regarded as separate tasks, which are accomplished in different network architectures when using deep learning-based methods. This study argues that these two tasks can be achieved using only one network with the same weights. We modify existing networks for stereo matching to perform the two tasks. We first enable the network capable of accepting both a single image and an image pair by duplicating the left image when the right image is absent. Then, we introduce a training procedure that alternatively selects training samples of depth prediction from a single image and binocular stereo matching. In this manner, the trained network can perform both tasks and single-image depth prediction even benefits from stereo matching to achieve better performance. Experimental results on KITTI raw dataset show that our model achieves state-of-the-art performances for accomplishing depth prediction from a single image and binocular stereo matching in the same architecture.

ProPaCoL-Net: A Novel Recursive Stereo Image SR Network with Progressive Parallax Coherency Learning

2020 ◽  
Vol 2020 (14) ◽  
pp. 342-1-342-8
Author(s):  
Jeonghun Kim ◽  
Munchurl Kim
Keyword(s):  
Autonomous Vehicles ◽  
Stereo Matching ◽  
Low Cost ◽  
Super Resolution ◽  
Stereo Image ◽  
Stereo Images ◽  
Computationally Efficient ◽  
Stereo Cameras ◽  
Left Image ◽  
Coherency Loss

Recently, stereo cameras have been widely packed in smart phones and autonomous vehicles thanks to low cost and smallsized packages. Nevertheless, acquiring high resolution (HR) stereo images is still a challenging problem. While the traditional stereo image processing tasks have mainly focused on stereo matching, stereo super-resolution (SR) has drawn less attention which is necessitated for HR images. Some deep learning based stereo image SR works have recently shown promising results. However, they have not fully exploited binocular parallax in SR, which may lead to unrealistic visual perception. In this paper, we present a novel and computationally efficient convolutional neural network (CNN) based deep SR network for stereo images by learning parallax coherency between the left and right SR images, which is called ProPaCoL-Net. The proposed ProPaCoL-Net progressively learns parallax coherency via a novel recursive parallax coherency (RPC) module with shared parameters. The RPC module is effectively designed to extract parallax information in prior for the left image SR from its right view input images and vice versa. Furthermore, we propose a parallax coherency loss to reliably train the ProPaCoL-Net. From extensive experiments, the ProPaCoL-Net shows to outperform the very recent state-of-the-art method with average 1.15 dB higher in PSNR.

scholarly journals A New Vehicle Localization Scheme Based on Combined Optical Camera Communication and Photogrammetry

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Md. Tanvir Hossan ◽  
Mostafa Zaman Chowdhury ◽  
Moh. Khalid Hasan ◽  
Md. Shahjalal ◽  
Trang Nguyen ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Image Sensor ◽  
Vehicle Localization ◽  
The Road ◽  
Localization Scheme ◽  
Image Area ◽  
Potential Benefits ◽  
On The Road ◽  
Vehicle Positioning ◽  
Optical Camera Communication

The demand for autonomous vehicles is increasing gradually owing to their enormous potential benefits. However, several challenges, such as vehicle localization, are involved in the development of autonomous vehicles. A simple and secure algorithm for vehicle positioning is proposed herein without massively modifying the existing transportation infrastructure. For vehicle localization, vehicles on the road are classified into two categories: host vehicles (HVs) are the ones used to estimate other vehicles’ positions and forwarding vehicles (FVs) are the ones that move in front of the HVs. The FV transmits modulated data from the tail (or back) light, and the camera of the HV receives that signal using optical camera communication (OCC). In addition, the streetlight (SL) data are considered to ensure the position accuracy of the HV. Determining the HV position minimizes the relative position variation between the HV and FV. Using photogrammetry, the distance between FV or SL and the camera of the HV is calculated by measuring the occupied image area on the image sensor. Comparing the change in distance between HV and SLs with the change in distance between HV and FV, the positions of FVs are determined. The performance of the proposed technique is analyzed, and the results indicate a significant improvement in performance. The experimental distance measurement validated the feasibility of the proposed scheme.

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