Abstract
Real time parking systems have a potential to reduce the congestion in crowded areas by providing real time indications of occupancy of parking spaces. To date, search systems are mostly implemented for indoor environments using costly sensor based techniques. Consequently, with the increasing demand for parking 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 images. The classifier was trained and tested why the features learned by the deep CNN (convolutional neural network) from public datasets (PKlot) having different illuminance and weather conditions. The indicates the great potential of this method to provide a low- cost and reliable solution to the parking systems in outdoor environments.
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 callMore From: International Journal of Innovative Research in Advanced Engineering
Disclaimer: 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.
