International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 04 | Apr42018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 922 Smart Parking system using Image processing Soundarya Rajesh 1 , Dr. M.G. Sumithra 2 1 Student, Department of Electronics and communication engineering, Bannari Amman Institute of Technology, Sathyamangalam-638401, India 2 Guide, Department of Electronics and communication engineering, Bannari Amman Institute of Technology, Sathyamangalam-638401, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - This project aims to design a smart parking system that is sensor free and functions as an indicator of the vacant spaces to the oncoming users of the parking lot. Keywords— sesnsor free, indicator, parking system I. INTRODUCTION An automatic parking system is used to make the whole process of parking cars more efficient and less complex for both drivers and administrators of the parking unit. This automation can be hardware based or entirely software based. An entirely software based system has not been employed so far. This project aims to replace the existing automated parking systems that rely heavily on various sensors for day to day operations with a system that relies more on software systems. The proposed system does not use any sensors, hence the mechanical and electronic liability of the system is reduced to a great extent. The proposed system just uses image processing algorithms to automate the parking with footage obtained from the parking lot’s surveillance cameras. These algorithms detect the empty parking spaces and convey the information to the drivers entering the parking premises. II. EXISTING SYSTEMS A. Existing methods of detection There are numerous methods of detecting cars in a car park such as Magnetic sensors, Microwave Radar, Ultrasonic sensors and image processing. This project discusses image processing. This is used because cameras can capture many cars at once making them efficient and inexpensive and this also adds to added security in parking lots that may prevent theft and damage of the vehicles . One or more cameras are used for video image processing depending on the area to be covered. Software is needed to process the images taken by the cameras. This processing is usually done by examining the difference between consecutive video frames. The area that a camera can scan can be easily changed by simply altering the position of the camera. There are two ways of using this system either by applying the edge detection with boundaries condition method for image detection module or applying point detection with canny operator method. In this project, the parking lot detection is done by identifying the green rounded image drawn at each parking lot. Matlab is used as a software platform. The process flowchart is shown in Figure 1. B. Process Flow chart Figure 1: The process flowchart III. THE PROCESS The process of identifying and processing whether the parking lot is empty or not entails the following : A. Image Identification First an image of the empty car park will be taken and stored for future references. The RGB value can be used to find where green circles are that represent empty car spaces if the system has enough processing power to process color images in a quick and efficient manner. If this is not the case, the image is converted into gray scale image. With these the system will know where to look for cars in the future. The color image is converted into a HSV image that describes the hue and saturation variations of the image. This enables the processor to be better able to distinguish the green circle based on the user’s input RGB value. The HSV image is simplified to a black and white binary image so that it is easier to deal with by making the pixels white where the threshold is more than 40%. In order to do this the HSV image needs to be converted to a gray scale format so that each pixel can be easily can compared with the threshold. This means if the pixel is less than the threshold the color of it will be black. Otherwise it will be white.