Read the Final Published Paper

The Problem

  • Prolonged parking overstays result in revenue loss for the parking company.
  • The current manual method of detecting parking occupancy is inefficient and requires parking officers.

Solution

Implement a high-mounted camera system with computer vision algorithms and deep learning to validate parking spot occupancy.

Key Steps:

  1. Grey Scaling and Gaussian Blur
  2. Background Subtraction
  3. Thresholding
  4. Dilation
  5. Non-Zero Pixels
  6. YOLO Neural Network

Steps

Results

Methods Accuracy Explanation
Background Subtraction Only 50% Detected every small movement
Background Subtraction + Thresholding 72% Filtered out small movements
Background Subtraction + Gaussian Blur + Thresholding + Dilation 84% Reduced noise from the image; still detected humans and other non-vehicle related movements
Addition of YOLO Neural Network 91% Eliminated non-vehicle related movement

The final achieved accuracy was 91%, surpassing previous research on this application 1.

Future Work

  • Further train YOLO to improve accuracy in classifying detected movements.
  • Implement multiple cameras to capture images from different angles, preventing small cars from being hidden by larger neighboring cars.
  • Develop a web app for parking companies to automate monitoring of their parking spots.