Year: 2025
Type: article
Abstract: Understanding curbside parking rules is crucial for drivers to quickly find legal curbside parking. Traditional data providers rely on manual methods to collect information about curbside parking signs, either by individually noting down sign details or downloading street-level imagery from digital maps. However, this process is slow and inadequate for building an accurate and up-to-date curbside parking rule database, considering the frequent updates to parking signs. In this article, we propose a novel deep-learning-based Inventory Management System (IMS) that automates to create and manage the curbside parking rule database using videos captured by off-the-shelf dashcams installed on vehicles. To the best of our knowledge, our system is the first of its kind to detect and interpret real-world curbside parking signs from videos and generate parking rules. By utilizing a serverless cloud architecture on AWS, IMS combines secure data retrieval, robust user authentication, and responsive map-based visualization, providing users with up-to-date parking information filtered by location, date, and time. Through real-world evaluations, we demonstrate that our system efficiently constructs an accurate curbside parking rule database enhancing urban mobility through efficient and informed parking management.
DOI: 10.1145/3733240
Authors: Jiayu Li, Putthida Samrith, Yin Jin, Deyang Zhong, Juhua Hu, Wei Cheng
Institution: University of Washington Tacoma
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409946039