Nighttime Traffic Light Location Based on Binocular Vision and Electronic Map

被引:0
|
作者
Lan Junfeng [1 ,2 ,3 ]
Fang Tiyu [1 ,2 ,3 ]
Li Jinping [1 ,2 ,3 ]
机构
[1] Univ Jinan, Sch Informat Sci & Engn, Jinan 250022, Peoples R China
[2] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Peoples R China
[3] Shandong Coll & Univ Key Lab Informat Proc & Cogn, Jinan 250022, Peoples R China
基金
中国国家自然科学基金;
关键词
Traffic light; Nighttime detection; Binocular vision; Electronic map; GPS position;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Autonomous driving needs to obtain various traffic information promptly to make decisions, and the accurate positioning of traffic light in real time is one of the key points to realize autonomous driving. Up to now, there are many interesting and practical researches related to the positioning. However, most of the scholars are doing researches of daylight traffic light location. At night, Neon lights, street lights, car lights and other factors may have a serious impact on the accurate positioning of traffic lights. How to remove the influence of these factors is a big challenge in autonomous driving. We propose a practical approach to address this challenge. The motivation of this approach is as follows: Firstly, GPS information and electronic map are used to estimate the distance and direction of the current position relative to the traffic light; secondly, binocular vision is applied to get the depth, orientation and other information of each area in the image, excluding the interference area outside the scope of the traffic light; thirdly, use some methods to positioning the traffic light, including brightness value division, geometric features analysis, circular degree detection, and then the status of the traffic light can be recognized through the HSV color space. The experimental result shows that the positioning accuracy of this method is more than 95%, the average of processing time for each image is 301ms, which can meet the requirements of accuracy and real-time.
引用
收藏
页码:6231 / 6236
页数:6
相关论文
共 50 条
  • [31] Flexible calibration method for line-structured light based on binocular vision
    Zhu, Ye
    Wang, Lianpo
    Gu, Yonggang
    Zhai, Chao
    Jin, Yi
    AOPC 2017: 3D MEASUREMENT TECHNOLOGY FOR INTELLIGENT MANUFACTURING, 2017, 10458
  • [32] BINOCULAR STEREO VISION MEASURING SYSTEM BASED ON STRUCTURED LIGHT EXTRACTION ALGORITHM
    Zhao, Lingli
    Xu, Haicheng
    Li, Junsheng
    Cai, Qun
    2012 INTERNATIONAL CONFERENCE ON INDUSTRIAL CONTROL AND ELECTRONICS ENGINEERING (ICICEE), 2012, : 644 - 647
  • [34] Physiological optics - Application of binocular vision to the location of a direction.
    de Gramont, A
    COMPTES RENDUS HEBDOMADAIRES DES SEANCES DE L ACADEMIE DES SCIENCES, 1929, 188 : 1120 - 1122
  • [35] Calibration and location analysis of a heterogeneous binocular stereo vision system
    Zhou, Haibo
    Li, Chenming
    Sun, Guoqing
    Yin, Jinming
    Ren, Fenglei
    APPLIED OPTICS, 2021, 60 (24) : 7214 - 7222
  • [36] Location-specific transfer from haptics to binocular vision
    Bruno, N
    Jacomuzzi, A
    PERCEPTION, 2003, 32 : 8 - 8
  • [37] 3-D Location of Tomato Based on Binocular Stereo Vision for Tomato Harvesting Robot
    Xiang, Rong
    Ying, Yibin
    Jiang, Huanyu
    Peng, Yongshi
    5TH INTERNATIONAL SYMPOSIUM ON ADVANCED OPTICAL MANUFACTURING AND TESTING TECHNOLOGIES: OPTOELECTRONIC MATERIALS AND DEVICES FOR DETECTOR, IMAGER, DISPLAY, AND ENERGY CONVERSION TECHNOLOGY, 2010, 7658
  • [38] Traffic Light Recognition and Ranging System Based on Machine Vision
    Zeng, Yiming
    Xie, Kanghui
    Yang, Ming
    Wu, Jun
    2019 INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE BIG DATA AND INTELLIGENT SYSTEMS (HPBD&IS), 2019, : 204 - 208
  • [39] Vision-based Traffic Light Detection for Intelligent Vehicles
    Du, Xiaoping
    Li, Yang
    Guo, Yuang
    Xiong, Hui
    2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE), 2017, : 1323 - 1330
  • [40] Fast Vision-based Pedestrian Traffic Light Detection
    Wu, Xue-Hua
    Hu, Renjie
    Bao, Yu-Qing
    IEEE 1ST CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2018), 2018, : 214 - 215