#19 Monitoring and Predicting Pedestrian Behavior Using Traffic Cameras
- Principal Investigator
- Luis E. Navarro-Serment
- Status
- Completed
- Start Date
- Jan. 1, 2017
- End Date
- Aug. 31, 2018
- Project Type
- Research Advanced
- Grant Program
- MAP-21 TSET National (2013 - 2018)
- Grant Cycle
- 2017 TSET UTC
- Visibility
- Public
Abstract
Effective traffic monitoring and control systems must consider all moving objects. While vehicle detection systems have become common, sensor-based solutions capable of detecting and monitoring pedestrian activity have yet to become an integral part of a smart and effective infrastructure capable of protecting these most vulnerable traffic participants. Such systems 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 currently ongoing research work we are developing a vision-based framework which is capable of detecting, tracking, and predicting the trajectories of people in real time. These abilities constitute the core of a system capable of monitoring any developments concerning pedestrian motion. In this project, we propose a natural extension of this work: we focus on the deployment aspects of this framework. We will address the challenges of bringing a new site into effective action, such as on-site camera calibration and the initial identification of a context to make predictions. The expected outcome is the creation, implementation, and demonstration of a framework which can be deployed in the field, and is capable of providing pedestrian information to systems like SURTRAC—a real time traffic signal control system. The milestones envisioned, to accomplish in one year, are: 1) Definition of methodology for initial station set up for on-site testing; 2) Design and test communications structure; 3) Live testing; and 4) Initial assessment of the impact on systems using pedestrian information. Our team is composed of a Master's student and researchers with many years of experience in computer vision for autonomous systems.
Description
Timeline
Strategic Description / RD&T
Deployment Plan
Expected Outcomes/Impacts
Expected Outputs
TRID
Individuals Involved
| Name | Affiliation | Role | Position | |
|---|---|---|---|---|
| hebert@cs.cmu.edu | Hebert, Martial | Robotics Institute | Co-PI | Faculty - Research/Systems |
| lenscmu@ri.cmu.edu | Navarro-Serment , Luis E. | Robotics Institute | PI | Faculty - Research/Systems |
Budget
Amount of UTC Funds Awarded
$127500.00Total Project Budget (from all funding sources)
$127500.00Documents
| Type | Name | Uploaded |
|---|---|---|
| Presentation | Calibration of traffic cameras using lowcost 3D scanners | April 18, 2017, 11:49 a.m. |
| Presentation | World_Model_Workshop_May-2017.pdf | Sept. 29, 2017, 3:01 p.m. |
| Progress Report | 19_Progress_Report_2017-09-30 | Sept. 29, 2017, 3:03 p.m. |
| Progress Report | 19_Progress_Report_2018-03-31 | March 30, 2018, 12:38 p.m. |
| Final Report | 019_finalReport.pdf | Nov. 28, 2018, 5:58 a.m. |
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