A Car-Following Data Collecting Method Based on Binocular Stereo Vision

Liu, Yongfei; Wang, Hao; Dong, Changyin; Chen, Quan

doi:10.1109/access.2020.2965833

Cited by 19 publications

(6 citation statements)

References 51 publications

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“…Liu [6] was the first researcher to construct face models by computer, specifically by using primary polygons to identify the human head and successfully implementing the first parametric model of the human face. Since Xue [7] proposed parametric models of faces in the 1970s, 3D face technology has developed rapidly in the following decades.…”

Section: Related Workmentioning

confidence: 99%

A 3D Face Modeling and Recognition Method Based on Binocular Stereo Vision and Depth-Sensing Detection

et al. 2022

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The human face is an important channel for human interaction and is the most expressive part of the human body with personalized and diverse characteristics. To improve the modeling speed as well as recognition speed and accuracy of 3D faces, this paper proposes a laser scanning binocular stereo vision imaging method based on binocular stereo vision and depth-sensing detection method. Existing matching methods have a weak anti-interference capability, cannot identify the spacing of objects quickly and efficiently, and have too large errors. Using the technique mentioned in this paper can remedy these shortcomings by mainly using the laser lines scanned onto the object as strong feature cues for left and right views for binocular vision matching and thus for depth measurement perception of the object. The experiments in this paper first explore the effect of parameter variation of the laser scanning binocular vision imaging system on the accuracy of object measurement depth values in an indoor environment; the experimental data are selected from 68 face feature points for modeling, cameras that photograph faces, stereo calibration of the cameras using calibration methods to obtain the parameters of the binocular vision imaging system, and after stereo correction of the left and right camera images, the laser line scan light is used as a pixel matching strong features for binocular camera matching, which can achieve fast recognition and high accuracy and then select the system parameters with the highest accuracy to perform 3D reconstruction experiments on actual target objects in the indoor environment to achieve faster recognition.

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Section: Related Workmentioning

confidence: 99%

A 3D Face Modeling and Recognition Method Based on Binocular Stereo Vision and Depth-Sensing Detection

et al. 2022

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“…The binocular ranging is a non-contact measurement based on parallax. According to the principle underlying it [ 27 , 28 , 29 ], as long as the camera’s parameters, and coordinates of the centroid of contours of the fruit in the left and right images can be obtained, ranging can be performed. This requires that the camera be calibrated to establish the relationship between pixels of the image and the position of the scene point.…”

Section: Methodsmentioning

confidence: 99%

A “Global–Local” Visual Servo System for Picking Manipulators

Shi

Zhang

et al. 2020

Sensors

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During the process of automated crop picking, the two hand–eye coordination operation systems, namely “eye to hand” and “eye in hand” have their respective advantages and disadvantages. It is challenging to simultaneously consider both the operational accuracy and the speed of a manipulator. In response to this problem, this study constructs a “global–local” visual servo picking system based on a prototype of a picking robot to provide a global field of vision (through binocular vision) and carry out the picking operation using the monocular visual servo. Using tomato picking as an example, experiments were conducted to obtain the accuracies of judgment and range of fruit maturity, and the scenario of fruit-bearing was simulated over an area where the operation was ongoing to examine the rate of success of the system in terms of continuous fruit picking. The results show that the global–local visual servo picking system had an average accuracy of correctly judging fruit maturity of 92.8%, average error of fruit distance measurement in the range 0.485 cm, average time for continuous fruit picking of 20.06 s, and average success rate of picking of 92.45%.

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“…To solve the two problems, several depth estimation approaches were proposed. The conventional stereo vision approaches [ 19 , 20 , 21 , 22 ] included CNN-based methods and video-based processing. Deep learning techniques markedly improved depth estimation performance on the KITTI dataset [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks

Wang

Lin

Chang

2021

Sensors

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In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to detect small objects in real time. For depth estimation, we introduce binocular vision to the monocular-based disparity estimation network, and the epipolar constraint is used to improve prediction accuracy. Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation. The results demonstrate that the proposed approach achieves better results compared to conventional methods.

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A Car-Following Data Collecting Method Based on Binocular Stereo Vision

Cited by 19 publications

References 51 publications

A 3D Face Modeling and Recognition Method Based on Binocular Stereo Vision and Depth-Sensing Detection

A 3D Face Modeling and Recognition Method Based on Binocular Stereo Vision and Depth-Sensing Detection

A “Global–Local” Visual Servo System for Picking Manipulators

Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks

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