Intelligent Traffic Light System Using Computer Vision with Android Monitoring and Control

被引:0
|
作者
Nodado, Jess Tyron G. [1 ]
Morales, Hans Christian P. [1 ]
Abugan, Ma Angelica P. [1 ]
Olisea, Jerick L. [1 ]
Aralar, Angelo C. [1 ]
Loresco, Pocholo James M. [1 ]
机构
[1] FEU Inst Technol, Elect & Elect Engn Dept, Manila, Philippines
关键词
Intelligent transportation system; Traffic light control; Image Processing; computer vision; Mobile Android-Based Application;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
One of the predominant cause of the diminishing productivity of the Philippines that affects its residents and industry sectors alike is no other than the unresolved traffic. Numerous efforts have been implemented in the country to regulate traffic including road expansion, highway development and application of several traffic schemes. One of the research thrust being studied is the solution to the limitation of traditional traffic light systems. Existing literatures in traffic light system embarked on intelligent transportation system (ITS) that is typically based its operation on real-time traffic density data, however implemented in limited control.- This paper discussed an approach in developing traffic signaling system capable of prioritizing congested lanes based on real-time traffic density data and integrated with an automated and manual control ported in a mobile android-based application. The system worked with CCTV cameras positioned at every lane of the intersection for the acquisition of traffic images transmitted to the Raspberry Pi 3 microcontroller for traffic density calculation using image processing. It utilized a traffic monitoring system and traffic lights operation control via a mobile android-based application. The system was tested and yielded an average of 92.83% and 85.77% vehicle detection rate for daytime and nighttime respectively. Moreover, an overall system reliability of 92.82% and 85.77% were obtained during daytime and nighttime testing based on the android GUI, lane prioritization and traffic light response. Future work involved integrating the Internet of Things (IoT) on the traffic light system for a wider scope interconnected implementation.
引用
收藏
页码:2461 / 2466
页数:6
相关论文
共 50 条
  • [41] DESIGN OF INTELLIGENT TRAFFIC LIGHT CONTROLLER USING EMBEDDED SYSTEM
    Chavan, Shilpa S.
    Deshpande, R. S.
    Rana, J. G.
    2009 SECOND INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING AND TECHNOLOGY (ICETET 2009), 2009, : 402 - +
  • [42] An Intelligent Traffic Control System
    Saleh, Aneesa
    Adeshina, Steve A.
    Galadima, Ahmad
    Ugweje, Okechukwu
    2017 13TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTER AND COMPUTATION (ICECCO), 2017,
  • [43] Bayesian network based computer vision algorithm for traffic monitoring using video
    Kumar, P
    Ranganath, S
    Huang, WM
    2003 IEEE INTELLIGENT TRANSPORTATION SYSTEMS PROCEEDINGS, VOLS. 1 & 2, 2003, : 897 - 902
  • [44] INTELLIGENT TRAFFIC CONTROL SYSTEM
    Bilal, Jubair Mohammed
    Jacob, Don
    ICSPC: 2007 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS, VOLS 1-3, PROCEEDINGS, 2007, : 496 - 499
  • [45] Application of Intelligent Traffic Scene Recognition Based on Computer Vision
    Lei, XiaoChun
    Li, Ren
    Lin, Kaihao
    WIRELESS SENSOR NETWORKS (CWSN 2021), 2021, 1509 : 97 - 110
  • [46] Train Rolling Stock Intelligent Monitoring with Computer Vision
    Kishore, P. V. V.
    Prasad, Ch. Raghava
    MATERIALS TODAY-PROCEEDINGS, 2017, 4 (02) : 1730 - 1739
  • [47] Intelligent elevator control by application of computer vision
    Vareljian, Vagram
    Zou, Ju Jia
    ADVANCES IN INTELLIGENT IT: ACTIVE MEDIA TECHNOLOGY 2006, 2006, 138 : 182 - 187
  • [48] Computer vision control of an intelligent forklift truck
    Garibotto, G
    Masciangelo, S
    Bassino, P
    Ilic, M
    IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, 1997, : 589 - 594
  • [49] Adaptive Traffic Light Controller Based on Congestion Detection Using Computer Vision
    Putra, R.G.
    Pribadi, W.
    Yuwono, I.
    Sudirman, D.E.J.
    Winarno, B.
    Journal of Physics: Conference Series, 2021, 1845 (01):
  • [50] Computer Vision on Embedded Sensors for Traffic Flow Monitoring
    Magrini, Massimo
    Moroni, Davide
    Palazzese, Giovanni
    Pieri, Gabriele
    Leone, Giuseppe Riccardo
    Salvetti, Ovidio
    2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, : 161 - 166