Vision Park - Next Gen Computer Vision for Efficient Parking Space Monitoring

The increasing number of vehicles and limited parking spaces in urban areas have highlighted the need for efficient parking space detection systems. Traditional methods, often reliant on manual monitoring or mobility sensors, face limitations in scalability and efficiency. This project proposes an innovative solution utilizing computer vision technology to automate the detection of occupied parking spaces. By deploying a camera to monitor the entire parking area, Deep learning models (CNN) are employed for object detection. The chosen approach involves creating masks to isolate specific regions of interest, followed by image segmentation using OpenCV. This method enhances accuracy and scalability compared to traditional techniques, offering a robust and automated parking space management solution. The study outlines the methodologies involved, with a focus on the development of the mask and image segmentation process.