Parking Slot Identification using Local Binary Pattern and Support Vector Machine

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.