Parking Stall Vacancy Indicator System Based on Deep Convolutional\n Neural Networks

2016/06/30 by Sepehr Valipour, Mennatullah Siam, Valipour, Sepehr +5
Computer Science · Engineering · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Enhancement Techniques #Impact of Light on Environment and Health #Smart Parking Systems Research

paper · pdf · doi:10.48550/arxiv.1606.09367

openalex publication_date 2016/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Parking management systems, and vacancy-indication services in particular,\ncan play a valuable role in reducing traffic and energy waste in large cities.\nVisual detection methods represent a cost-effective option, since they can take\nadvantage of hardware usually already available in many parking lots, namely\ncameras. However, visual detection methods can be fragile and not easily\ngeneralizable. In this paper, we present a robust detection algorithm based on\ndeep convolutional neural networks. We implemented and tested our algorithm on\na large baseline dataset, and also on a set of image feeds from actual cameras\nalready installed in parking lots. We have developed a fully functional system,\nfrom server-side image analysis to front-end user interface, to demonstrate the\npracticality of our method.\n

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