As urban environments grow and become even more complex, businesses need highly accurate location intelligence technology to stay ahead. Building a scalable network that detects and identifies objects as fast as your brain starts with the vision of the vehicle. Forward-facing cameras and radar will soon be standard equipment in all cars. This project aims at every car learning from every car, car Parking place identification and continuing to enable an autonomous world with the help of tracking device (precise, end-to-end tracking and accurate, real-time, and historical locations for devices, people, and things). By combining open data with proprietary sources and technologies car sensor data and AI, the HEAR location platform offers a uniquely complete location data set along. The raw images captured by cameras may contain noises, the lighting of workspaces, a flickering of light sources. The preprocessing includes filtering out the noises, images conversions into different color spaces, blurring the image, edge detection, line detection, circle detection. The current project paper comprises of the development of image processing based parking space management. This project will implement based on the theories of Background subtraction algorithm. The usage of this algorithm will be used as a mapping method to reduce the error of detecting the vehicle. The explanation of algorithms such as Background subtraction algorithm and the implementation of Open CV as software to manipulate the image program will be used throughout the project