Car Parking Space Detection Using OpenCV

EasyChair Preprint 9625

Abstract

The problem of finding an appropriate parking space is a challenging one, particularly in large cities. With the increase in car ownership, parking spaces have become scarce. The growing demand for these spots coupled with limited availability has led to imbalances between supply and demand. A lack of adequate parking management systems has resulted in many streets being littered with illegally parked cars. A scalable, reliable, and efficient parking management system is needed to combat this problem. Deep learning-based computer vision techniques have emerged as promising solutions for such problems. These technologies have had a huge impact on the field of image recognition and processing. They also present great potential for further applications in the area of vehicle tracking. Hence, they can be used to detect parking spots. A densely packed city center can be an unbearable place to park your car. Finding parking spaces can prove frustrating if you're not careful. Automatic smart parking systems promise to ease the burden of finding a spot in busy areas. To help drivers find a parking spot, we have developed a vision-based smartparking framework. First, we divided the parking lot into blocks and categorized each block to determine whether it was occupied or empty. Then we sent information about the availability of free or reserved parking to motorists on their smartphones. Our system demonstrates superior performance compared to commercially available solutions because it offers higher accuracy

Keyphrases: Automatic parking, Slot recognition, machine learning, parking management, parking space detection

@booklet{EasyChair:9625,
  author    = {Rahul Tekam and Shoheb Shaikh and Leela Bitla and Pranav Rathi and Hiamnshu Chambhare},
  title     = {Car Parking Space Detection Using OpenCV},
  howpublished = {EasyChair Preprint 9625},
  year      = {EasyChair, 2023}}