Automated Vehicle Parking Occupancy Detection in Real-Time

Abstract

Parking occupancy detection systems help to identify the available parking spaces and direct vehicles efficiently to unoccupied lots by reducing time and energy. This paper presents an approach for the design and development of an end-to-end automated vehicle parking occupancy detection system. The novelty of this study lies in the methodology followed for the object detection process using RetinaNet one stage detector and region-based convolutional neural network deep learning technique. The proposed software architecture consists of low coupled components that support scalability and reliability. The developed web-based and mobile-based client applications assist to find parking spaces easily and efficiently. The existing solutions utilize dedicated sensors and depend on manual segmentation of surveillance footage to detect the state of parking spaces. The proposed approach eliminates existing limitations while maintaining reasonable accuracy.