Using Deep Neural Networks to Quantify Parking Dwell Time

TL;DR

This paper presents a deep learning approach combining classification and Siamese networks to estimate parking dwell times from images, addressing challenges like low resolution and weather effects in smart city environments.

Contribution

It introduces a novel method that integrates two neural networks for parking dwell time estimation, demonstrating its effectiveness in cross-dataset scenarios.

Findings

75% perfect dwell time predictions with an ideal classifier

49% perfect predictions using a real-world classifier

Siamese network performance depends on classifier accuracy

Abstract

In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transportation solutions. However, automatically determining individual car dwell times from images faces challenges, such as images collected from low-resolution cameras, lighting variations, and weather effects. In this work, we propose a method that combines two deep neural networks to compute the dwell time of each car in a parking lot. The proposed method first defines the parking space status between occupied and empty using a deep classification network. Then, it uses a Siamese network to check if the parked car is the same as the previous image. Using an experimental protocol that focuses on a cross-dataset scenario, we show that if a perfect classifier is used, the proposed system generates 75% of perfect dwell time predictions, where the predicted value matched exactly the time the car stayed parked. Nevertheless, our experiments show a drop in prediction quality when a real-world classifier is used to predict the parking space statuses, reaching 49% of perfect predictions, showing that the proposed Siamese network is promising but impacted by the quality of the classifier used at the beginning of the pipeline.