Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks

Parking-management systems, including services that recognize vacant stalls, can play a valuable role in reducing traffic and energy waste in large cities. Visual methods for detecting vacant parking spots are cost-effective options since they can take advantage of the cameras already available in many parking lots. However, visual-detection methods can be fragile and not easily generalizable. In this paper, we present a robust detection algorithm based on deep convolutional neural networks. We implemented and tested our algorithm on a large baseline dataset, and also tested on video feeds from web-accessible parking-lot cameras. Our detection method improved the state of the art AUC by 8.13%. It also showed robust performance in different testing scenarios including tests on public cameras. We have developed a fully functional system, from server-side image analysis to front-end user interface, to demonstrate the practicality of our method.