Abstract
One component of smart city is smart transportation, known as Intelligent Transportation Systems (ITS). In this study, we discuss the estimation of moving vehicle speed based on video processing using the Euclidean Distance method. In this study, we examine the effect of camera angles on the video acquisition to speed estimation accuracy. In addition, Region of Interest (ROI) will be designed into three parts to determine which area is the most appropriate to be chosen, so that the estimated vehicle speed will be better. These approaches have never been studied by previous researchers. The separation between the background and foreground is conducted using Gaussian Mixture Models method. By comparing the displacement distance and the number of frames per second (fps), we obtain speed estimate for each vehicle. According to the experimental results, our system can estimate the speed of the vehicle with an accuracy of 99.38%.
Highlights
Smart city is defined as a concept of developing and managing cities by utilizing Information and Communication Technology (ICT) to connect, monitor, and control various resources within the city
This study discussed the estimation of vehicle speed at different camera tilt angles with the lowest accuracy of 87.01% and the highest was 99.38% and resulted in a consistent level of accuracy for estimating consistent multi-object vehicle speeds
By classifying the test data into three types, namely low, medium, and high speed that the camera angle can affect the accuracy of the results of speed estimation
Summary
Smart city is defined as a concept of developing and managing cities by utilizing Information and Communication Technology (ICT) to connect, monitor, and control various resources within the city. Analysis on the angle of shooting and division of Region Of Interest (ROI) areas has never been studied, even though it can affect accuracy in vehicle speed detection. We divide the ROI region and we observe the influence on the accuracy of the results, in order to obtain the proper vehicle speed estimation. It turns out that the position of ROI with different shooting angles ( the angle difference is small), greatly affects the accuracy of the results, so the contribution of this research is how to set the camera angle, if it will be used for vehicle speed detection on certain roads, for example in Indonesia. The final section of this paper contains conclusions and research that will be carried out in the future
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