Using OpenCV Space Detection System

In Python, OpenCV is the open-source library for image processing, machine learning, and video capturing. It is an important part in real-time operation. With the use of the OpenCV, we can efficiently use the images as well as videos to identify the objects, faces, and handwriting of an object. We will only focus to object discovery from images utilizing OpenCV in this paper. It is especially a very useful library in Python for image capturing and for all the other processes. The growing number of vehicles and congestion in parking areas have led to significant difficulties for people in finding suitable parking spaces. To address this issue, researchers have been drawn to the emerging field of automatic smart parking systems. These systems utilize technology to assist drivers in locating and reserving parking spots. Our team has developed a vision-based smart parking system that surpasses the accuracy of existing hardware solutions. We have named it Counting Available Parking Space using Image Processing (CAPSuIP). This low-cost system utilizes a modified Software Development Life Cycle (SDLC) to efficiently plan, analyze, and test its functionalities. The effective use of parking spots in urban settings is essential for reducing congestion and maximizing resource use. The Space Detection System (SDS) presented in this project was created using the OpenCV computer vision framework. The main goal of the system is to monitor and evaluate parking lots, automatically identifying and classifying each parking space's occupancy state. The primary elements and features of the SDS are described in this abstract.