Project PKLot 2.0 - Machine Learning Applied in Smart Cities
This project aims to propose novel techniques that leverage the use of cameras to enhance automated traffic management in smart cities through machine learning. It addresses issues such as the automatic detection of parking spaces, the identification of areas congested with vehicles, and the generation of relevant information, such as the utilization time of specific road infrastructure in urban areas.
The solutions devised in this project could, for instance, assist in guiding drivers to the nearest available parking spot, saving time and fuel. Moreover, these solutions open the possibility of creating new business opportunities, such as automated charging based on the duration a vehicle occupies a particular parking space.
Main Technologies
Deep Learning, Instance Segmentation, Image Processing, and IoT Devices.
Project Team
Alceu de Souza Britto Jr. (PUCPR)
Andre Hochuli (PUCPR)
Eduardo Cunha de Almeida (UFPR)
Luiz Eduardo S. de Oliveira (UFPR)
Paulo R. Lisboa de Almeida (UFPR)
Bruno Aquiles
Luan Kujavski
Marcelo Ribas
Heloísa Mendes
Current Results
Automated mapping of parking area layout.
Our method, as presented in IEEE SMC 2023, has the capability to automatically identify parking areas through cameras, entirely free from human intervention. This enables us to efficiently map and subsequently monitor these regions within smart cities with minimal effort.
In the video below, you can observe the system in action, employing a fusion of instance segmentation networks and image processing to automatically process images from a typical workday. This process allows for a comprehensive understanding of the area layout.