Automated Parking Space Detection Using Convolutional Neural Networks

TL;DR

This paper presents a real-time parking space detection system using CNNs trained on a specific dataset, achieving 99% accuracy and demonstrating robustness across different parking lots.

Contribution

The paper introduces a CNN-based approach for real-time parking space detection with high accuracy and cross-dataset robustness, utilizing Caffe and Nvidia DiGITS frameworks.

Findings

Achieved 99% accuracy on validation set.

Maintained 99% accuracy on a foreign dataset.

Demonstrated robustness of the system across different parking lots.

Abstract

Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time and energy. We have used computer vision techniques to infer the state of the parking lot given the data collected from the University of The Witwatersrand. This paper presents an approach for a real-time parking space classification based on Convolutional Neural Networks (CNN) using Caffe and Nvidia DiGITS framework. The training process has been done using DiGITS and the output is a caffemodel used for predictions to detect vacant and occupied parking spots. The system checks a defined area whether a parking spot (bounding boxes defined at initialization of the system) is containing a car or not (occupied or vacant). Those bounding box coordinates are saved from a frame of the video of the parking lot in a JSON format, to be later used by the system for sequential prediction on each parking spot. The system has been trained using the LeNet network with the Nesterov Accelerated Gradient as solver and the AlexNet network with the Stochastic Gradient Descent as solver. We were able to get an accuracy on the validation set of 99\% for both networks. The accuracy on a foreign dataset(PKLot) returned as well 99\%. Those are experimental results based on the training set shows how robust the system can be when the prediction has to take place in a different parking space.