Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems
Image and video processing have been widely used to provide traffic parameters, which will be used to improve certain areas of traffic operations. This research aims to develop a model for estimating vehicle speed from video streams to support traffic flow analysis (TFA) systems. Subsequently, this...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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JOIV
2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/10491/1/J16056_63a9f1205a1b5cb523ed59f37644040b.pdf |
| _version_ | 1796870178156314624 |
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| author | Maizatul Najihah Arriffin, Maizatul Najihah Arriffin Salama A. Mostafa, Salama A. Mostafa Umar Farooq Khattak, Umar Farooq Khattak Mustafa Musa Jaber, Mustafa Musa Jaber Zirawani Baharum, Zirawani Baharum Defni, Defni Taufik Gusman, Taufik Gusman |
| author_facet | Maizatul Najihah Arriffin, Maizatul Najihah Arriffin Salama A. Mostafa, Salama A. Mostafa Umar Farooq Khattak, Umar Farooq Khattak Mustafa Musa Jaber, Mustafa Musa Jaber Zirawani Baharum, Zirawani Baharum Defni, Defni Taufik Gusman, Taufik Gusman |
| author_sort | Maizatul Najihah Arriffin, Maizatul Najihah Arriffin |
| collection | UTHM |
| description | Image and video processing have been widely used to provide traffic parameters, which will be used to improve certain areas of traffic operations. This research aims to develop a model for estimating vehicle speed from video streams to support traffic flow analysis (TFA) systems. Subsequently, this paper proposes a vehicle speed estimation model with three main stages of achieving speed
estimation: (1) pre-processing, (2) segmentation, and (3) speed detection. The model uses a bilateral filter in the pre-processing strategy to provide free-shadow image quality and sharpen the image. Gaussian filter and active contour are used to detect and track objects of interest in the image. The Pinhole model is used to assess the real distance of the item within the image sequence for speed estimation. Kalman filter and optical flow are used to flatten vehicle speed and acceleration uncertainties. This model is evaluated with a dataset that consists of video recordings of moving vehicles at traffic light junctions on the urban roadway. The average percentage for speed estimation error is 20.86%. The average percentage for accuracy obtained is 79.14%, and the overall average precision of 0.08. |
| first_indexed | 2024-03-05T22:05:41Z |
| format | Article |
| id | uthm.eprints-10491 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:05:41Z |
| publishDate | 2023 |
| publisher | JOIV |
| record_format | dspace |
| spelling | uthm.eprints-104912023-11-27T07:24:38Z http://eprints.uthm.edu.my/10491/ Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems Maizatul Najihah Arriffin, Maizatul Najihah Arriffin Salama A. Mostafa, Salama A. Mostafa Umar Farooq Khattak, Umar Farooq Khattak Mustafa Musa Jaber, Mustafa Musa Jaber Zirawani Baharum, Zirawani Baharum Defni, Defni Taufik Gusman, Taufik Gusman T Technology (General) Image and video processing have been widely used to provide traffic parameters, which will be used to improve certain areas of traffic operations. This research aims to develop a model for estimating vehicle speed from video streams to support traffic flow analysis (TFA) systems. Subsequently, this paper proposes a vehicle speed estimation model with three main stages of achieving speed estimation: (1) pre-processing, (2) segmentation, and (3) speed detection. The model uses a bilateral filter in the pre-processing strategy to provide free-shadow image quality and sharpen the image. Gaussian filter and active contour are used to detect and track objects of interest in the image. The Pinhole model is used to assess the real distance of the item within the image sequence for speed estimation. Kalman filter and optical flow are used to flatten vehicle speed and acceleration uncertainties. This model is evaluated with a dataset that consists of video recordings of moving vehicles at traffic light junctions on the urban roadway. The average percentage for speed estimation error is 20.86%. The average percentage for accuracy obtained is 79.14%, and the overall average precision of 0.08. JOIV 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10491/1/J16056_63a9f1205a1b5cb523ed59f37644040b.pdf Maizatul Najihah Arriffin, Maizatul Najihah Arriffin and Salama A. Mostafa, Salama A. Mostafa and Umar Farooq Khattak, Umar Farooq Khattak and Mustafa Musa Jaber, Mustafa Musa Jaber and Zirawani Baharum, Zirawani Baharum and Defni, Defni and Taufik Gusman, Taufik Gusman (2023) Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems. INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION, 7 (2). pp. 295-300. |
| spellingShingle | T Technology (General) Maizatul Najihah Arriffin, Maizatul Najihah Arriffin Salama A. Mostafa, Salama A. Mostafa Umar Farooq Khattak, Umar Farooq Khattak Mustafa Musa Jaber, Mustafa Musa Jaber Zirawani Baharum, Zirawani Baharum Defni, Defni Taufik Gusman, Taufik Gusman Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems |
| title | Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems |
| title_full | Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems |
| title_fullStr | Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems |
| title_full_unstemmed | Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems |
| title_short | Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems |
| title_sort | vehicles speed estimation model from video streams for automatic traffic flow analysis systems |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10491/1/J16056_63a9f1205a1b5cb523ed59f37644040b.pdf |
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