Computer Vision-Based Vehicle Allotment System using Perspective Mapping

TL;DR

This paper introduces a cost-effective smart parking system leveraging computer vision and perspective mapping to accurately detect vacant spaces and guide users in urban environments.

Contribution

It presents a novel parking system combining YOLOv8 object detection with inverse perspective mapping for dynamic, multi-view parking space assessment.

Findings

Uses computer vision for accurate vehicle and space detection

Employs IPM to merge multi-camera views effectively

Provides a 3D visualization for user guidance

Abstract

Smart city research envisions a future in which data-driven solutions and sustainable infrastructure work together to define urban living at the crossroads of urbanization and technology. Within this framework, smart parking systems play an important role in reducing urban congestion and supporting sustainable transportation. Automating parking solutions have considerable benefits, such as increased efficiency and less reliance on human involvement, but obstacles such as sensor limitations and integration complications remain. To overcome them, a more sophisticated car allotment system is required, particularly in heavily populated urban areas. Computer vision, with its higher accuracy and adaptability, outperforms traditional sensor-based systems for recognizing vehicles and vacant parking spaces. Unlike fixed sensor technologies, computer vision can dynamically assess a wide range of visual inputs while adjusting to changing parking layouts. This research presents a cost-effective, easy-to-implement smart parking system utilizing computer vision and object detection models like YOLOv8. Using inverse perspective mapping (IPM) to merge images from four camera views, we extract data on vacant spaces. The system simulates a 3D parking environment, representing available spots with a 3D Cartesian plot to guide users.