2020 | vol. 68 | nr. 2 | art. 8

Public Transportation Identification System in Prohibited Areas based on Traffic Signs using Image Processing

Youllia Indrawaty NURHASANAH, Afriyanti Dwi KARTIKA, Novi NUR’AINI
Abstract
Growth in the transportation sector in urban areas was very high.  This resulted in an increase in the number of vehicles and congestion on the highway. Public Transportation was a vehicle that causes a lot of traffic jam and made the road feel more crowded. In addition, public transportation also often violated rules such as entering into a residential housing lane to avoid overcrowding of vehicles on the highway. The violation of public transportation was by entering and passing through the residential area which was an area that must be free from the flow of public transportation. In addition, there were also traffic signs to prohibit public transportation from passing. To overcome this problem, this study proposed a system to identify and distinguish types of public transportation from other types of vehicles based on colour images. The system processes were carried out through several stages, including offline video capture, video frame processes, background and foreground separation (background subtraction), morphology (opening), bitwise and, images rectangular crop, HSV colour space conversion and HSV histogram creation. The results of the HSV colour space conversion in the histogram were used for the identification process in determining whether the city transportation existed or not by using Learning Vector Quantization method. The results of the system testing using Learning Vector Quantization method with 30Fps frame rate video test data were capable to recognize 66 images of public transportation and not public transportation from 78 videos of car typed of vehicle, obtained success with a percentage of 84.62%. And 60Fps frame rate video test data was able to recognize 71 images of public transportation and not public transportation from 78 videos of car typed of vehicle, obtained success with a percentage of 91.03%.
Keywords: public transportation, traffic signs, prohibited area, image processing, identification
To cite this article: Yi. Nurhasanah, Ad. Kartika, N. Nur’aini, “Public transportation identification system in prohibited areas based on traffic signs using image processing”, in Electrotehnica, Electronica, Automatica (EEA), 2020, vol. 68, no. 2, pp. 73-84, ISSN 1582-5175.

 

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