Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems

The project is focused on developing a deep learning based data acquisition and analytics tool using vision - based sensors (i.e., cameras) to understand cities with machine eyes . The team will assess the maturity of various smart city applications using computer vision and object detection (e.g., pedestrian detection, work zone identification , curb lane usage, connected and automated vehicles [CAVs] ) as well as the needs of the local agencies. The goal is to demonstrate the c ost - effectiveness of the computer vision technology to generate new stream of mobility data and provide support for planning and operational strategies , utilizing both existing transportation infrastructure and emerging probe and CAVs . More specifically, t his project aims to establish an inventory of available traffic camera systems in the U.S. and deploy two computer vision smart city applications based on stakeholder feedback that are customized for New York City (NYC) . The team will also establish a formalized pipeline for running the computer vision algorithm enhanced for NYC conditions and prototype the applications for real - world implementation.