Monitoring and Predicting Pedestrian Behavior at Traffic Intersections

Source Organization:
University:Carnegie Mellon University
Principal Investigator:Luis E. Navarro-Serment
PI Contact
Project Manager:Courtney Ehrlichman
Funding Source(s) and Amounts Provided (by each agency or organization):$80,000
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Start and End Dates:January 2016 - January 2017
Project Status:Active
Subject Categories:Infrastructure-based Technologies
Abstract:Effective traffic monitoring systems must take into account all moving objects. While vehicle detection systems are common, sensor-based solutions that provide awareness of pedestrian activity have yet to become an integral part of a smart and effective infrastructure capable of protecting these most vulnerable traffic participants. Such a system could alert incoming vehicles about dangerous situations involving pedestrians, or provide adaptive traffic light control systems with information about the motion of people, so that they can operate and make decisions cognizant of all moving objects. In our previous research work we have developed a vision-based framework which is capable of detecting, tracking, and predicting the trajectories of people. These abilities constitute the core of a system capable of monitoring any developments concerning pedestrian motion. In this project, we aim at positioning our research for deployment. We plan to enhance our algorithms to make them suitable for operation in real traffic situations, in terms of accuracy and robustness against adverse operating conditions. Additionally, we will address the difficulties of bringing a new site into effective action, such as camera calibration and the initial identification of a context to make predictions. The expected outcome is an implementation of the enhanced framework which can be deployed in the field, and is capable of providing pedestrian information to Surtrac1—a real time traffic signal control system. The milestones envisioned, to accomplish in two years, are: 1) Development of enhanced approach for pedestrian detection; 2) Implementation of prototype suitable for operation in real time; and 3) Testing and characterization of prototype in the field. Our team is composed of a Master's student, and researchers with many years of experience in computer vision for autonomous systems.
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