Development of FPGA based smart traffic light controller system with image processing
As the population of the city, as well as the number of automobiles travel on the roads are increasing day by day, traffic congestion at the junctions is becoming a huge problem for many major cities nowadays. One of the reasons behind this traffic issue is the inefficiency of the techniques and alg...
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| Format: | Final Year Project / Dissertation / Thesis |
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2019
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| Online Access: | http://eprints.utar.edu.my/3942/ http://eprints.utar.edu.my/3942/1/fyp_EE_2019_TKS.pdf |
| _version_ | 1848886032026566656 |
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| author | Tan, Keng Shuen |
| author_facet | Tan, Keng Shuen |
| author_sort | Tan, Keng Shuen |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | As the population of the city, as well as the number of automobiles travel on the roads are increasing day by day, traffic congestion at the junctions is becoming a huge problem for many major cities nowadays. One of the reasons behind this traffic issue is the inefficiency of the techniques and algorithms used in the existing traffic light system which unable to adapt to the continuous changing traffic situation and eventually lead to traffic congestion spreads and occurrence of road accidents increase. Therefore, to prevent the situations further deteriorate, there is a pressing need for the introduction of advanced system and technology that able to improve the current traffic light control system to better accommodate this increasing demand.
In this project, an FPGA based Smart Traffic Light Control System (STLCS) is proposed with the aid of Computer Vision techniques in traffic signal control. The developed STLCS is comprised two development boards; Altera DE2 board and Raspberry Pi 3B+. Altera DE2 board is mainly used to execute the automated traffic light signalling system while the Raspberry Pi 3B+ is utilized to in-charge the computer vision tasks, for instance, video acquisition, processing, segmentation, object detection, etc. In this system, a wide view angle digital camera is fixed at the intersection road for real-time monitoring on four intersecting roads. A specific traffic lane will be captured when the Raspberry Pi received a request signal from the Altera DE2 board. The captured image of a particular road is processed through a series of image processing techniques for vehicle detection and count. After this, the detected number of vehicles is further used to calculate the proper time duration for controlling
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| first_indexed | 2025-11-15T19:32:02Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-3942 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:32:02Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-39422021-01-08T07:38:44Z Development of FPGA based smart traffic light controller system with image processing Tan, Keng Shuen T Technology (General) TK Electrical engineering. Electronics Nuclear engineering As the population of the city, as well as the number of automobiles travel on the roads are increasing day by day, traffic congestion at the junctions is becoming a huge problem for many major cities nowadays. One of the reasons behind this traffic issue is the inefficiency of the techniques and algorithms used in the existing traffic light system which unable to adapt to the continuous changing traffic situation and eventually lead to traffic congestion spreads and occurrence of road accidents increase. Therefore, to prevent the situations further deteriorate, there is a pressing need for the introduction of advanced system and technology that able to improve the current traffic light control system to better accommodate this increasing demand. In this project, an FPGA based Smart Traffic Light Control System (STLCS) is proposed with the aid of Computer Vision techniques in traffic signal control. The developed STLCS is comprised two development boards; Altera DE2 board and Raspberry Pi 3B+. Altera DE2 board is mainly used to execute the automated traffic light signalling system while the Raspberry Pi 3B+ is utilized to in-charge the computer vision tasks, for instance, video acquisition, processing, segmentation, object detection, etc. In this system, a wide view angle digital camera is fixed at the intersection road for real-time monitoring on four intersecting roads. A specific traffic lane will be captured when the Raspberry Pi received a request signal from the Altera DE2 board. The captured image of a particular road is processed through a series of image processing techniques for vehicle detection and count. After this, the detected number of vehicles is further used to calculate the proper time duration for controlling 2019-09-03 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/3942/1/fyp_EE_2019_TKS.pdf Tan, Keng Shuen (2019) Development of FPGA based smart traffic light controller system with image processing. Final Year Project, UTAR. http://eprints.utar.edu.my/3942/ |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Tan, Keng Shuen Development of FPGA based smart traffic light controller system with image processing |
| title | Development of FPGA based smart traffic light controller system with image processing |
| title_full | Development of FPGA based smart traffic light controller system with image processing |
| title_fullStr | Development of FPGA based smart traffic light controller system with image processing |
| title_full_unstemmed | Development of FPGA based smart traffic light controller system with image processing |
| title_short | Development of FPGA based smart traffic light controller system with image processing |
| title_sort | development of fpga based smart traffic light controller system with image processing |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utar.edu.my/3942/ http://eprints.utar.edu.my/3942/1/fyp_EE_2019_TKS.pdf |