Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 251-254, 259.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Large-scale Automatic Driving Scene Reconstruction Based on Binocular Image

LI Yin-guo, ZHOU Zhong-kui, BAI Ling   

  1. (College of Computer Science and Technology,Chongqing University of Posts & Telecommunications,Chongqing 400065,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: The large-scale smart driving scene reconstruction can feedback the surrounding road traffic environment information for the vehicle control system in the vehicle driving environment,and realize the visualization of the environmental information.At present,the existing three-dimensional reconstruction scheme is mainly oriented to thestructuredscene,and it is difficult to meet the real-time performance required by the smart driving system while ensuring a certain precision which can make when the three-dimensional reconstruction of the large-scale unstructured smart driving scene is performed.In order to solve this problem,a three-dimensional scene reconstruction method based on binocular vision is proposed.Firstly,by optimizing the stereo matching strategy,the stereo matching efficiency is improved,and then the uniform distance feature point extraction algorithm RSD is proposed to reduce the time consumption of 3D point cloud computing and triangulation,and the real-time performance of large-scale smart driving scene reconstruction is improved.The experimental results prove the effectiveness of this algorithm,which can be used to reconstruct the scene of large-scale smart driving scene,and can meet the demand of intelligent driving system in real-time.

Key words: Intelligent driving scene reconstruction, Binocular vision, Feature extraction, Stereo matching, Depth value calculation

CLC Number: 

  • TP391.41
[1]谢志萍,雷莉萍.智能网联汽车环境感知技术的发展和研究现状[J].成都工业学院学报,2016,19(4):87-92.
[2]李克强,戴一凡,李升波,等.智能网联汽车(ICV)技术的发展现状及趋势[J].汽车安全与节能学报,2017,8(1):1-14.
[3]ZHANG X Y,GAO H B,GUO M,et al.A study on key technologies of unmanned driving[J].CAAI Transactions on Intelligence Technology,2016,1(1):4-13.
[4]陈辉,马世伟,Andreas Nuechter.基于激光扫描和SFM的非同步点云三维重构方法[J].仪器仪表学报,2016,37(5):1148-1157.
[5]徐超,李乔.基于计算机视觉的三维重建技术综述[J].数字技术与应用,2017,1(34):54-56.
[6]孙宇阳.基于单幅图像的三维重建技术综述[J].北方工业大学学报,2011,23(1):9-13.
[7]黄鹏程,江剑宇,杨波.双目立体视觉的研究现状及进展[J].光学仪器,2018,40(4):81-86.
[8]BAYKANT B,ALAGO Z.Obtaining Depth Maps FromColorImages By Region Based Stereo Matching Algorithms[J].OncuBilim Algorithm And Systems Labs,2008,8(4):122-134.
[9]何人杰.双目立体视觉区域局部匹配算法的改进及其实现[J].现代电子技术,2009,32(12):68-70.
[10]肖志涛,卢晓方,耿磊,等.基于极线校正的亚像素相位立体匹配方法[J].红外与激光工程,2014,43(S1):225-230.
[11]YU L,ZHANG D R, HOLDEN E J.A fast and fully automatic registration approach based on point features for multi-source remote-sensing images[J].Computers and Geosciences,2007,34(7):838-848.
[12]HARRIS C,STEPHENS M J.A combined corner and edge detector[C]∥Proceedingsof Fourth Alvey Vision Conference.Manchester.England:IEEE,1998:147-151.
[13]LOWE D G.Distinctive Image Features from Scale-InvariantKeypoints[J].International Journal of Computer Vision,2004,60(2):92-109.
[14]BAY H,ESS A,TUYTELAARS T,et al.Speeded-Up Robust Features (SURF)[J].Computer Vision and Image Understanding,2007,110(3):346-359.
[15]RUBLEE E, RABAUD V, KONOLIGE K,et al.ORB:an efficient alternative to SIFT or SURF[C]∥IEEE International Conference on Computer Vision.2011:2564-2571.
[16]MUJAM,LOWE D G.Fast approximate nearest neighborswith automaticalgorithm configuration[C]∥Proceedingsof IEEE Conference on Computer Vision Theory and Applications.Lisbon,Portugal:IEEE Computer Society,2009:331-340.
[17]CANDÉS E J,ROMBERG J K,TAO T.Stable signal recovery from incomplete and inaccurate measurements[J].Communications on Pure and Applied Mathematics,2006,59(8):1-15.
[18]吕耀文,康凯.基于双目视觉的三维重建和拼接技术研究[J].光电子技术,2016,36(4):237-241.
[19]汪神岳,刘强,王超然,等.基于双目立体相机的室外场景三维重建系统设计[J].计算机测量与控,2017,25(11):137-140.
[20]谢增广.平面点集Delaunay三角剖分的分治算法[J].计算机工程与设计,2012,33(7):2652-2658.
[21]姜翰青,王博胜,章国锋,等.面向复杂三维场景的高质量纹理映射[J].计算机学报,2015,38(12):2349-2360.
[1] ZHOU Yan, ZENG Fan-zhi, WU Chen, LUO Yue, LIU Zi-qin. 3D Shape Feature Extraction Method Based on Deep Learning [J]. Computer Science, 2019, 46(9): 47-58.
[2] DU Zhen, MA Li-peng, SUN Guo-zi. Network Traffic Anomaly Detection Based on Wavelet Analysis [J]. Computer Science, 2019, 46(8): 178-182.
[3] SHE Rong-rong, ZHANG Li-ping. Method for Identifying and Recommending Reconstructed Clones Based on Software Evolution History [J]. Computer Science, 2019, 46(8): 224-232.
[4] HAN Hui,WANG Li-ming,CHAI Yu-mei,LIU Zhen. Text Sentiment Classification Based on Deep Forests with Enhanced Features [J]. Computer Science, 2019, 46(7): 172-179.
[5] ZHAO Zi-yang, JIANG Mu-rong, HUANG Ya-qun, HAO Jian-yu, ZENG Ke. Single Image Depth Estimation Algorithm Based on SFS and Binocular Model [J]. Computer Science, 2019, 46(6A): 161-164.
[6] LI Yue-feng. 3D Retrieval Algorithm Based on Multi-feature [J]. Computer Science, 2019, 46(6A): 266-269.
[7] HAN Xiao, ZHANG Jing, LI Yue-long. Gesture Recognition Based on Hand Geometric Distribution Feature [J]. Computer Science, 2019, 46(6A): 246-249.
[8] HE Xiao-wen, HU Yi-fei, WANG Hai-ping, CHEN Mo. Online Learning Nonnegative Matrix Factorization [J]. Computer Science, 2019, 46(6A): 473-477.
[9] ZHOU Bin-bin, ZHANG Hong-jun, ZHANG Rui, FENG Yun-tian, XU You-wei. Construction of Military Corpus for Entity Annotation [J]. Computer Science, 2019, 46(6A): 540-546.
[10] MENG Zhi-qing, XU Wei-wei. Temporal Text Data Stream Feature Trend Model and Algorithm [J]. Computer Science, 2019, 46(6A): 417-422.
[11] XU Lei, WANG Jian-xin. Data Mining Algorithm of Abnormal Network Based on Fuzzy Neural Network [J]. Computer Science, 2019, 46(4): 73-76.
[12] LIU Xiao-hong, ZHU Yu-quan, LIU Zhe, SONG Yu-qing, ZHU Yan, YUAN De-qi. Liver CT Image Feature Extraction Method Based on Improved Multi-scale LBP Algorithm [J]. Computer Science, 2019, 46(3): 125-130.
[13] LI Yin-min, XUE Kai-xin, GAO Zan, XUE Yan-bin, XU Guang-ping, ZHANG Hua. 3-D Model Retrieval Algorithm Based on Residual Network [J]. Computer Science, 2019, 46(3): 148-153.
[14] CHEN Wei, LIU Yan, LEI Qing. Classification of Small Difference Behavior Characteristics Based on Intelligent Vision [J]. Computer Science, 2019, 46(3): 298-302.
[15] SHENG Lei, WEI Zhi-hua, ZHANG Peng-yu. Multi-layer Object Detection Algorithm Based on Multi-source Feature Late Fusion [J]. Computer Science, 2019, 46(2): 249-254.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] DU Wei, DING Shi-fei. Overview on Multi-agent Reinforcement Learning[J]. Computer Science, 2019, 46(8): 1 -8 .
[2] GAO Li-zheng, ZHOU Gang, LUO Jun-yong, LAN Ming-jing. Survey on Meta-event Extraction[J]. Computer Science, 2019, 46(8): 9 -15 .
[3] CAI Li, LI Ying-zi, JIANG Fang, LIANG Yu. Study on Clustering Mining of Imbalanced Data Fusion Towards Urban Hotspots[J]. Computer Science, 2019, 46(8): 16 -22 .
[4] YANG Zhen, WANG Hong-jun. Important Location Identification of Mobile Users Based on Trajectory Division and Density Clustering Method[J]. Computer Science, 2019, 46(8): 23 -27 .
[5] DENG Cun-bin, YU Hui-qun, FAN Gui-sheng. Integrating Dynamic Collaborative Filtering and Deep Learning for Recommendation[J]. Computer Science, 2019, 46(8): 28 -34 .
[6] ZHONG Feng-yan, WANG Yan, LI Nian-shuang. Node Selection Scheme for Data Repair in Heterogeneous Distributed Storage Systems[J]. Computer Science, 2019, 46(8): 35 -41 .
[7] SUN Guo-dao, ZHOU Zhi-xiu, LI Si, LIU Yi-peng, LIANG Rong-hua. Spatio-Temporal Evolution of Geographical Topics[J]. Computer Science, 2019, 46(8): 42 -49 .
[8] ZHANG Hui-bing, ZHONG Hao, HU Xiao-li. User Reviews Clustering Method Based on Topic Analysis[J]. Computer Science, 2019, 46(8): 50 -55 .
[9] LI Bo-jia, ZHANG Yang-sen, CHEN Ruo-yu. Method for Generating Massive Data with Assignable Distribution[J]. Computer Science, 2019, 46(8): 56 -63 .
[10] LU Xian-guang, DU Xue-hui, WANG Wen-juan. Alert Correlation Algorithm Based on Improved FP Growth[J]. Computer Science, 2019, 46(8): 64 -70 .