Open AccessVehicle Speed Estimation Using Gaussian Mixture Model and Kalman Filter
Author(s) -
Ahmed Abdulwahab Tayeb,
Rabah W. Aldhaheri,
Muhammad Shehzad Hanif
Publication year - 2021
Publication title -
international journal of computers, communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2021.4.4211
Subject(s) - computer science , background subtraction , kalman filter , mixture model , frame (networking) , artificial intelligence , computer vision , speed limit , process (computing) , real time computing , pixel , telecommunications , archaeology , history , operating system
Many countries use traffic enforcement camera to monitor the speed limit and capture over speed violations. The main objective of such a system is to enforce the speed limits which results in the reduction of number of accidents, fatalities, and serious injuries. Traditionally, the task is carried out manually by the enforcement agencies with the help of specialized hardware such as radar and camera. To automate the process, an efficient and robust solution is needed. Vehicle detection, tracking and speed estimation are the main tasks in an automated system which are not trivial. In this paper, we address the problem of vehicle detection, tracking, and speed estimation using a single fixed camera. A background subtraction method based on the Gaussian Mixture Model (GMM) is employed to detect vehicles because of its capability in dealing with complex backgrounds and variations in the appearance due to illumination and scale. Next, the detected vehicles are tracked in each frame by using the Kalman Filter. Finally, an estimate the speed of each vehicle is determined by using the perspective geometry model. The complete system is tested at our university campus and the results are promising.