Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks
This addresses parking management to reduce traffic and energy waste in cities, but it is incremental as it builds on existing visual detection methods.
The paper tackled the problem of detecting parking stall vacancies using visual methods by developing a robust detection algorithm based on deep convolutional neural networks, and it was tested on a large baseline dataset and real camera feeds to demonstrate practicality.
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.