Parking Space Detection Using Textural Descriptors

In this paper we assess the use of textural de-scriptors for the problem of parking space detection. We focus our experiments on two descriptors (Local Binary Patterns and Local Phase Quantization) that have attracted a great deal of attention because of their outstanding performance in a number of applications. We show through a series of comprehensive experiments that both descriptors are able to achieve very low error rates on a database composed of 105,837 images of parking spaces. We also show that the combination of the diverse classifiers developed in this work can bring further improvement achieving an error rate of 0.16%. The results reached in this work compare favorably to other published methods.