CSEIT206276 | Accepted : 01 April 2020 | Published : 07 April 2020 | March-April-2020 [ 6 (2) : 241-246 ] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2020 IJSRCSEIT | Volume 6 | Issue 2 | ISSN : 2456-3307 DOI : https://doi.org/10.32628/CSEIT206276 241 Cost effective Parking System Using Computer Vision Kaushal Shah, Shivang Rajbhoi, Nikhil Prasad, Charmi Patel, Roushan Raj Computer Science and Engineering, Parul Institute of Technology, Vadodara, Gujarat, India ABSTRACT This paper presents an approach for detecting real-time parking slots which includes vision-based techniques. Traditional sensor-based systems are not cost effective as 'n' number of sensors are required for 'n' parking slots. Transmitting sensor data to central system is done by hardwiring or installing dedicated wireless system which is again costly. Our technique will overcome this problem by using camera instead of number of sensors which is expensive. For detection we are using a Convolutional Neural Networks (CNN) classifier which is custom trained. It is more robust and effective in changing light conditions and weather. The following system do not require high processing as detections are done on static images not on video stream. We have also demonstrated real-time parking scenario by constructing a small prototype which shows practical implementation of our system. Keywords : Convolutional Neural Networks, You Only Look Once, Deep learning. I. INTRODUCTION Since last 10 years, there is a huge increase in number of vehicles. But the current transportation infrastructure and car park facilities are insufficient in sustaining the influx of vehicles on the road. Therefore, problems such as traffic congestion and insufficient parking space inevitably crops up [1]. Car parking occupancy detection are of great importance for an effective management of car parking lots. Knowing in real-time availability of free parking spaces and communicating to the users can be of great help in reducing the queues, improve scalability and the time required to find an empty space in a parking lot [2]. An important requirement for these systems is the ability to detect nearby parking slots automatically, which is becoming an increasingly difficult and critical task and therefore the traffic increases [3]. Various measures have been taken in the attempts to overcome the traffic problems. Some methods like sensor-based and computer vision classifiers are functional nowadays to reduce the problems regarding parking. But these systems are either very costly or less efficient and less robust. Current detection systems repurpose classifiers to perform detection. To detect an object, these systems take a classifier for that object and evaluate it at various locations and scales in a test image. Systems like deformable parts models (DPM) use a sliding window approach where the classifier is run at evenly spaced locations over the entire image [4]. We are using YOLO (You Only Look Once), which is an object detection algorithm based on Convolutional Neural Network (CNN). YOLO is refreshingly fast as well as simple and YOLO trains on full images and directly optimizes detection performance. Since we frame detection as a regression problem, we don’t need a complex pipeline. We simply run our neural network on a new image at test time to predict detections [5]. YOLO reasons globally about the image when making predictions. Unlike sliding window and region proposal-based techniques, YOLO sees the entire image during training and test time so it