Real-time image-based parking occupancy detection using deep learning

Parking Guidance and Information (PGI) systems have a potential to reduce the congestion in crowded areas by providing real-time indications of occupancy of parking spaces. To date, such systems are mostly implemented for indoor environments using costly sensor-based techniques. Consequently, with the increasing demand for PGI systems in outdoor environments, inexpensive image-based detection methods have become a focus of research and development recently. Motivated by the remarkable performance of Convolutional Neural Networks (CNNs) in various image category recognition tasks, this study presents a robust parking occupancy detection framework by using a deep CNN and a binary Support Vector Machine (SVM) classifier to detect the occupancy of outdoor parking spaces from images. The classifier was trained and tested by the features learned by the deep CNN from public datasets (PKLot) having different illuminance and weather conditions. Subsequently, we evaluate the transfer learning performance (the ability to generalise results to a new dataset) of the developed method on a parking dataset created for this research. We report detection accuracies of 99.7% and 96.7% for the public dataset and our dataset respectively, which indicates the great potential of this method to provide a low-cost and reliable solution to the PGI systems in outdoor environments.