Vehicles Speed Estimation Model from Video Streams for Automatic Traffic Flow Analysis Systems

Maizatul Najihah Arriffin - Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Salama A. Mostafa - Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Umar Farooq Khattak - UNITAR International University, kelana jaya, Petaling Jaya, Selangor, Malaysia
Mustafa Musa Jaber - Dijlah University College, Baghdad, Iraq
Zirawani Baharum - Universiti Kuala Lumpur, Bandar Seri Alam, Johor Bahru, Malaysia
- Defni - Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia
Taufik Gusman - Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia

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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.


Traffic flow analysis; vehicle speed estimation; Kalman filter; Pinhole model; bilateral filter

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