Opencv in Smart Parking Space Allocation and Management

Abstract

In today’s world, the quantity of cars on the road remains steadily rising at a quickening pace. Due to the growing sum of automobiles on the road also the imperfect number of parking spaces, it can be a pain and a waste of time to look for a parking spot. This is because there are fewer parking spots than there are cars. The upshot of this is traffic congestion on the roads. According to studies that were carried out in this region, motorists typically spend 15 minutes driving about in search of a parking spot and cover a distance of 0.5 miles while travelling at a speed of 10 mph. The use of intelligent parking solutions has the potential to significantly cut down on the severity of these difficulties. Because it requires a large number of sensors to be installed at each parking lot, the traditional method is not only expensive but also time-consuming and labour-intensive. The results of this study present a smart parking classification that is based on image dispensation and may be utilised in a number of scenarios, including open parking lots, parking garages with many levels, and other similar places. In order to ascertain whether or not a parking spot in the gathered video is occupied, the proposed design for the system utilises a combination of edge detection and coordinate bound pixel sections. In addition to that, it functions as an illustration of the process of transforming text into images. Tesseract is utilised whenever an image is analysed in order to extract the text contained within it. It is possible to modify the strength of the image processing so that each photograph experiences precisely the amount of processing that is necessary to provide the highest quality text results.