  Parking management systems, and vacancy-indication services in particular,
can play a valuable role in reducing traffic and energy waste in large cities.
Visual detection methods represent a cost-effective option, since they can take
advantage of hardware usually already available in many parking lots, namely
cameras. 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 on a set of image feeds from actual cameras
already installed in parking lots. We have developed a fully functional system,
from server-side image analysis to front-end user interface, to demonstrate the
practicality of our method.
