Image Segmentation and Processing for Efficient Parking Space Analysis
This work addresses the problem of efficient parking space analysis for urban management, but it appears incremental as it builds on existing image processing techniques with specific improvements.
The paper tackles the problem of detecting vacant parking spaces in challenging conditions like uneven illumination and distorted lines by developing an image processing method using MATLAB, which reduces costs by eliminating the need for individual sensors and processes groups of slots together.
In this paper, we develop a method to detect vacant parking spaces in an environment with unclear segments and contours with the help of MATLAB image processing capabilities. Due to the anomalies present in the parking spaces, such as uneven illumination, distorted slot lines and overlapping of cars. The present-day conventional algorithms have difficulties processing the image for accurate results. The algorithm proposed uses a combination of image pre-processing and false contour detection techniques to improve the detection efficiency. The proposed method also eliminates the need to employ individual sensors to detect a car, instead uses real-time static images to consider a group of slots together, instead of the usual single slot method. This greatly decreases the expenses required to design an efficient parking system. We compare the performance of our algorithm to that of other techniques. These comparisons show that the proposed algorithm can detect the vacancies in the parking spots while ignoring the false data and other distortions.