Vehicle speed detection based on Gaussian mixture model using sequential of images

Budi Setiyono, Dwi Ratna Sulistyaningrum, Soetrisno, Farah Fajriyah, Danang Wahyu Wicaksono

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)

Abstract

Intelligent Transportation System is one of the important components in the development of smart cities. Detection of vehicle speed on the highway is supporting the management of traffic engineering. The purpose of this study is to detect the speed of the moving vehicles using digital image processing. Our approach is as follows: The inputs are a sequence of frames, frame rate (fps) and ROI. The steps are following: First we separate foreground and background using Gaussian Mixture Model (GMM) in each frames. Then in each frame, we calculate the location of object and its centroid. Next we determine the speed by computing the movement of centroid in sequence of frames. In the calculation of speed, we only consider frames when the centroid is inside the predefined region of interest (ROI). Finally we transform the pixel displacement into a time unit of km/hour. Validation of the system is done by comparing the speed calculated manually and obtained by the system. The results of software testing can detect the speed of vehicles with the highest accuracy is 97.52% and the lowest accuracy is 77.41%. And the detection results of testing by using real video footage on the road is included with real speed of the vehicle.

Original languageEnglish
Article number012144
JournalJournal of Physics: Conference Series
Volume890
Issue number1
DOIs
Publication statusPublished - 21 Sept 2017
Event1st International Conference on Applied and Industrial Mathematics and Statistics 2017, ICoAIMS 2017 - Kuantan, Pahang, Malaysia
Duration: 8 Aug 201710 Aug 2017

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