Abstract
Parking space availability has become common problem in many big cities. This problem occurs due to fast growing of vehicle ownership. Therefore, the demand of parking area in big cities is also increased. An information system on parking space availability may help the driver to find accurately the parking location. This real-time system can avoid the drivers waste their time in looking the available parking space. This paper aims to implement Local Binary Pattern (LBP) as a method for extracting distinguishable features of the vacant and occupied parking slot. Support Vector Machine (SVM) classifier is used to differentiate the status of parking slot, either vacant or occupied. Combination of LBP and SVM has been tested on a total of 7, 670 sample images. Validation result shows that the proposed algorithm can provide a high accuracy, 96.8%, in classification of parking slot availability.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
