Platform Pittsburgh: Improving City Quality of Life Through City Scale Computing
Platform Pittsburgh is a collaborative effort between researchers at Carnegie Mellon University, the City of Pittsburgh, and other partners with the goal of creating a "living laboratory" for conducting smart city research and analytics. As Pittsburgh continues to adopt state of the art technology, this project harnesses the power of edge computing and visual data. The goal is to lay the groundwork for a testbed in order to develop applications and produce statistics towards improving quality of life. We hope to foster interdiscplinary collaborations along with feedback from the community to solve real-world challenges in transportation mobility and safety.
An integrated approach will be taken that uses computer vision, machine learning, and simulations to produce data to city planners, traffic engineers, and decision makers. Research will be directed towards applications that improve efficiency, health, safety, and overall quality-of-life, such as:
- Transportation and City Dynamic
- Climate and Environmental Monitorin
- Infrastructure to Vehicle Communicatio
- Responsible Privacy and Data Usage Policy
In addition to creating new algorithms and software for civic use, one of the outputs of the CMU Urban Data Analytics Testbed is to establish responsible guidelines for capture, use, and retention of urban video data. Throughout the project, we will be working with privacy and public policy experts at CMU to establish these guidelines.
Project Update (April 2020)
A resource for developing and testing algorithms at the edge is available to researchers in multiple disciplines (computer vision, privacy, security, systems). An extensive database of images from multiple cameras in an intersection has been collected to develop algorithms for estimating air quality, and understanding vehicle dynamics and human activity.
Computational methods were also developed for detecting and tracking pedestrians and vehicles. The exhaust tailpipe of vehicles was detectable to estimate CO2 emissions, the accuracy of the methods compared to state-of-the-art methods in the literature, and the impact gauged by presenting to potential collaborators and discussions.
Privacy concerns for deploying cameras in public arrose, so the team established procedures to protect privacy and relayed them to the public alleviated those concerns. The team found a great need and desire to have a platform for developing research methodologies that can be put into practice because relying on external parties like comcast caused several delays that caused some delay in producing results.
Visit the project website here: http://platformpgh.cs.cmu.edu/