Rectangular Empty Parking Space Detection using SIFT based Classification

Abstract

In this paper, we describe a method of combining rectangle detection and scale invariant feature transform (SIFT) analysis for empty parking space detection. A parking space in a parking lot is represented as a rectangular region of pixels in an image captured from an aerial camera. Detecting rectangular parking spaces in a new image involves an alternating scheme of extracting peaks from the Radon transform for the whole image and filtering them against specific geometric and spatial constraints. We then compute SIFT descriptors from these detected rectangular parking spaces and further apply supervised classification methods for detecting empty parking spaces. We demonstrate the performance of our model on several synthetic and real data.