Density Based Smart Traffic Control System Using Canny Edge Detection Algorithm for Congregating Traffic Information

被引:0
|
作者
Tahmid, Taqi [1 ]
Hossain, Eklas [2 ]
机构
[1] Khulna Univ Engn & Technol, Dept EEE, Khulna 9203, Bangladesh
[2] Oregon Tech, Dept Elect Engn & Renewable Energy, Klamath Falls, OR 97601 USA
关键词
Smart Traffic Control; Density based Traffic Control; Edge Detection; Image Processing in Traffic Control;
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
As the problem of urban traffic congestion intensifies, there is a pressing need for the introduction of advanced technology and equipment to improve the state-of-the-art of traffic control. The current methods used such as timers or human control are proved to be inferior to alleviate this crisis. In this paper, a system to control the traffic by measuring the real-time vehicle density using canny edge detection with digital image processing is proposed. This imposing traffic control system offers significant improvement in response time, vehicle management, automation, reliability and overall efficiency over the existing systems. Besides that, the complete technique from image acquisition to edge detection and finally green signal allotment using four sample images of different traffic conditions is illustrated with proper schematics and the final results are verified by hardware implementation.
引用
收藏
页数:5
相关论文
共 50 条
  • [21] Traffic Light Detection with Color and Edge Information
    Omachi, Masako
    Omachi, Shinichiro
    2009 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, VOL 2, 2009, : 284 - +
  • [22] IoT-based traffic prediction and traffic signal control system for smart city
    S. Neelakandan
    M. A. Berlin
    Sandesh Tripathi
    V. Brindha Devi
    Indu Bhardwaj
    N. Arulkumar
    Soft Computing, 2021, 25 : 12241 - 12248
  • [23] Smart Traffic Monitoring System Using Computer Vision and Edge Computing
    Liu, Guanxiong
    Shi, Hang
    Kiani, Abbas
    Khreishah, Abdallah
    Lee, Joyoung
    Ansari, Nirwan
    Liu, Chengjun
    Yousef, Mustafa Mohammad
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (08) : 12027 - 12038
  • [24] IoT-based traffic prediction and traffic signal control system for smart city
    Neelakandan, S.
    Berlin, M. A.
    Tripathi, Sandesh
    Devi, V. Brindha
    Bhardwaj, Indu
    Arulkumar, N.
    SOFT COMPUTING, 2021, 25 (18) : 12241 - 12248
  • [25] Development of Smart Street Light System and Density based Traffic System using Internet of Things
    Ranjitha, L.
    Kumar, Ananda K. S.
    Kavitha, H. L.
    Harshitha, K. R.
    Manisha, C.
    2020 5TH IEEE INTERNATIONAL CONFERENCE ON RECENT TRENDS ON ELECTRONICS, INFORMATION, COMMUNICATION & TECHNOLOGY (RTEICT-2020), 2020, : 247 - 251
  • [26] Design of a Traffic Density Management and Control System for Smart City Applications
    Deshmukh, Prashant
    Gupta, Devashish
    Das, Santos Kumar
    Sahoo, Upendra Kumar
    COGNITIVE INFORMATICS AND SOFT COMPUTING, 2020, 1040 : 457 - 468
  • [27] Smart Traffic Light Control System
    Ghazal, Bilal
    ElKhatib, Khaled
    Chahine, Khaled
    Kherfan, Mohamad
    2016 THIRD INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, COMPUTER ENGINEERING AND THEIR APPLICATIONS (EECEA), 2016, : 140 - 145
  • [28] Smart City Traffic Control System
    Adwani, Kakan
    Rakesh, N.
    INTELLIGENT COMMUNICATION TECHNOLOGIES AND VIRTUAL MOBILE NETWORKS, ICICV 2019, 2020, 33 : 421 - 430
  • [29] Smart Traffic Congestion Control System
    Balu, Shibin
    Priyadharsini, C.
    PROCEEDINGS OF THE 2019 3RD INTERNATIONAL CONFERENCE ON COMPUTING METHODOLOGIES AND COMMUNICATION (ICCMC 2019), 2019, : 689 - 692
  • [30] Object Edge Detection Algorithm Based on Improved Canny Algorithm
    Yu Xinshan
    Meng Xiangyin
    Jin Tengfei
    Luo Jinze
    LASER & OPTOELECTRONICS PROGRESS, 2023, 60 (22)