Open AccessParking detection system using background subtraction and HSV color segmentation
Author(s) -
Awang Hendrianto Pratomo,
Wilis Kaswidjanti,
Alek Setiyo Nugroho,
Shoffan Saifullah
Publication year - 2021
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v10i6.3251
Subject(s) - background subtraction , computer vision , artificial intelligence , pixel , thresholding , hsl and hsv , computer science , object detection , process (computing) , segmentation , parking lot , color space , preprocessor , filter (signal processing) , shadow (psychology) , image (mathematics) , engineering , virus , civil engineering , virology , biology , operating system , psychology , psychotherapist
Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.