Vacancy state detector oriented to convolutional neural network, background subtraction and embedded systems

Abstract

Much has been discussed recently related to population ascension, the reasons for this event, and in particular, the aspects of society affected. Over the years, the city governments realized a higher level of growth, mainly in terms of urban scale, technology, and individuals numbers. It comprises improvements and investments in their structure and policies, motivated by improving conditions in population live quality and reduce environmental, energy, fuel, time, and money resources, besides population living costs, including the increasing demand for parking structures accessible to the general or private-public, and a waste of substantial daily time and fuel, disturbing the population routinely. Therefore, one way to achieve that challenge is focused on reducing energy, money,and time costs to travel to work or travel to another substantial location. That work presents a robust, and low computational power Smart Parking system adaptive to several environments changes to detect and report vacancy states in a parking space oriented to Deep Learning, and Embedded Systems. This project consists of determining the parking vacancy status through statistical and image processing methods, creates a robust image data set, and the Convolutional Neural Network model focused on predict three final classes. In order to save computational power, this approach uses the Background Subtraction based on the Mixture of Gaussian method, only updating parking space status, in which large levels of motion are detected. The proposed model presents 94 percent of precision at the designed domain.