Vehicle and Parking Slot Detection using Image Processing and Open CV

Abstract

This paper provides a smart parking system based on open CV that was created for open parking lots, multi-story parking garages, and many more applications. The suggested system design makes use of the coordinate bound pixel and combined bound pixel and combined edge detection sections in python and the open CV library to determine whether or not a parking space in the captured footage is occupied. Additionally, it shows how to convert images to text. Text is extracted from the processed image using Tesseract. To ensure that various slots receive varying degrees of treatment to yield optimal text results, image processing is variable.One of the key functionalities of these systems is automated occupancy detection. Through the use of edge detection and pixel analysis techniques provided by OpenCV, the system can determine in real-time whether a parking spot is occupied or vacant. This information is crucial for drivers seeking available spaces and for parking facility managers to monitor occupancy levels efficiently.The variable level of image processing ensures adaptability across different environments and lighting conditions. By adjusting the image processing algorithms, the system can effectively handle variations in image quality and environmental factors, providing consistent and reliable results regardless of the setting.

Introduction

With the rapid increase in vehicle ownership, finding parking spaces in urban areas has become a significant challenge, causing traffic congestion, pollution, and inefficiency. Traditional parking methods are manual, time-consuming, and ineffective, especially in multi-storey parking structures.

To address this, smart parking systems leveraging image processing and computer vision offer an efficient solution. These systems use cameras to capture real-time images of parking lots and apply advanced algorithms—such as edge detection, background subtraction, contour detection, and machine learning classifiers—to detect and monitor parking space occupancy. Integration of machine learning improves adaptability to varying conditions and vehicle types, while Optical Character Recognition (OCR) can be used to recognize license plates and signage, enabling automated billing and enforcement.

Various related works highlight the use of embedded vision systems, IoT-based sensors (e.g., ZigBee, geomagnetic sensors), and cloud integration to enhance parking management and provide real-time occupancy data via mobile apps or displays.

The proposed system uses Python and OpenCV to analyze video feeds, define parking zones, and mark spaces as occupied (red) or vacant (green). It also employs OCR (Tesseract) for license plate recognition to automate billing based on parking duration.

Benefits include:

• Reduced time searching for parking, easing congestion

• Real-time parking information accessible to users

• Cost savings for parking operators through automation

• Environmental benefits via reduced vehicle idling and emissions

The system’s methodology involves image acquisition, preprocessing, segmentation, feature extraction, classification, and real-time occupancy updates. Algorithms like background subtraction, edge and contour detection, machine learning classifiers, and OCR form the core of the implementation.

In testing, the system successfully identifies empty parking spaces by marking them green and occupied spaces red, demonstrating practical effectiveness.

Conclusion

Image Processing is very crucial for extracting any informa-tion from an image. In this study, a proposed plan for a smart parking system based on image processing has been effectively tried and run with a few videos taken from indoor parking garages. The system works precisely in deciding regardless of whether the parking slots are occupied or not by showing a red outline if a vehicle is inside or consuming a parking spot and afterward turns green when it is unoccupied. In the number plate detection, we first applied image processing algorithms to images and afterward those images were utilized in Tesseract software to acquire the text from the images. Different images have distinctive text styles, length, width and font, so different images require different levels of digital image processing techniques. Out of these image results for a single image the most appropriate image is then applied to Tesseract for obtaining the text from an image. Therefore, after digitally processing the image we have accomplished better and near to perfection outputs. The future scope of smart parking systems using image pro-cessing holds tremendous potential for further advancements and integration with emerging technologies.Implementation of advanced security measures, such as encryption and secure data transmission protocols, to protect user information and ensure the integrity of system operations. Privacy concerns related to image data handling will also be addressed through robust policies and technologies.

References

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Copyright

Copyright © 2025 B. Kavya Sri, Kethavath Lalitha, U. Abhishek, Ch. Vijayendar Reddy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.