Fast Generation Methods of Around View Monitoring Images for Automobiles Based on 3D Space Sphere

被引：0
|
作者：
Cao L.-B. [1 ,2 ]
Xia J.-H. [1 ]
Liao J.-C. [1 ]
Zhang G.-J. [1 ]
Zhang R.-F. [3 ]
机构：
[1] State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, Hunan
[2] Institute at Shenzhen, Hunan University, Shenzhen, 518063, Guangdong
[3] Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518000, Guangdong
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2020年 / 33卷 / 01期
基金：
中国国家自然科学基金;
关键词：
3D space spherical surface; Advanced driver assistance system; Algorithm optimization; Around view monitoring; Image fusion; Imaging direction reconstruction; Traffic engineering;
D O I：
10.19721/j.cnki.1001-7372.2020.01.016
中图分类号：
学科分类号：
摘要：
To efficiently generate around view monitoring (AVM) images with low distortion for the ultimate purpose of guaranteeing real-time performance in advanced driver assistance systems (ADASs) based on image information, this study proposed an optimization method for AVM systems. First, a new method based on scanning line and circle fitting was designed to extract the image area from a fisheye camera accurately and automatically. To guarantee the method's robustness, parameters were analyzed and optimized. The radius and center were calculated according to the image area of the fisheye camera. A Cartesian coordinate system was constructed with the center of the circle as the origin and the unit length as the radius. Then, fisheye camera images were mapped to a 3D space spherical surface by latitude-longitude projection based on the Cartesian coordinate system, and a virtual camera was constructed in the center of the sphere. The optimal rotation angle of the visual cone was calculated according to the gradient descent. Based on the rotation angle, the imaging direction of the virtual camera was rebuilt to obtain an aerial view directly, where the perspective transformation and fisheye calibration were combined into a single step to improve computational efficiency and reduce the distortion of the system. Finally, placing the image of each virtual camera in a specified location and fusing four images to reduce the loss of image information as a result of seams. The results demonstrate that the speed of AVM image generation nearly doubles with the same platform and produces less distortion in images. The proposed algorithm can speed up the generation rate of images in an AVM system based on camera parameters and can reduce both computing resource usage and loss of image information. Thus, the algorithm can be used to improve real-time performance and reliability of ADASs based on information obtained from AVM images. © 2020, Editorial Department of China Journal of Highway and Transport. All right reserved.
引用
收藏
页码：153 / 162and171
相关论文
共 22 条
  • [1] Li L., Li C.L., Zhang Q.S., Et al., Automatic Parking Slot Detection Based on Around View Monitor (AVM) SystemsIEEE, 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1-6, (2017)
  • [2] Suhr J.K., Jung H.G., Fully-automatic Recognition of Various Parking Slot Markings in Around View Monitor (AVM) Image Sequences, 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 1294-1299, (2013)
  • [3] Choi Y.W., Kim K.Y., Park M.R., Et al., 2015 Development of Around View System Based on Moving Object Detection for Safe Bus Getting on and off, IEEE International Symposium on Consumer Electronics, pp. 1-2, (2015)
  • [4] Kum C.H., Cho D.C., Ra M.S., Et al., Lane Detection System with Around View Monitoring for Intelligent Vehicle, 2013 International SOC Design Conference (ISOCC), pp. 215-218, (2014)
  • [5] Shimizu S., Taniguchi S., Kidena T., Et al., Multi Angle Vision System to Supplement Driver's Visual Field, Journal of Fujitsu Ten Technological, 36, pp. 12-18, (2011)
  • [6] Liu X.-F., Research on Fisheye Camera Based 360 Degree Panorama Driving-assist System, (2016)
  • [7] Bao Q.-Q., Research on Algorithm of Vehicle Panoramic System Based on Projection to Variable Curved Surface, (2015)
  • [8] Kuo T.Y., Wang Y.S., Cheng Y.J., Et al., 3D Around View Monitoring System for Automobiles, 2017 IEEE International Conference on Consumer Electronics (ICCE), pp. 271-272, (2017)
  • [9] Zhao J.H., Gao H.B., Zhang X.Y., Et al., Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving, Journal of Robotics, 2, pp. 1-9, (2018)
  • [10] Jeon B., Park G., Lee J., Et al., A Memory-efficient Architecture of Full HD Around View Monitor Systems, IEEE Transactions on Intelligent Transportation Systems, 15, 6, pp. 2683-2695, (2014)
123