| |
| Principal
Investigators: |
| Neal
Checka |
| David
Demirdjian |
| |
| The
Problem: |
| The
goal of this project is to design and build a multi-person
tracking system using a network of stereo cameras. Our
system, which stands in an ordinary conference room
is able to track people, estimate their trajectories
as well as different characteristics (e.g. size, posture). |
|
| Motivation: |
| Systems
which can track and understand people have a wide variety
of commercial applications. It is predicted that computers
of the future will interact more naturally with humans
than they do now. Instead of the desktop computer paradigm
with humans communicating by typing, computers of the
future will be able to understand human speech and movements.
Our system demonstrates the capabilities of a solely
vision-based system for these ends. |
| |
| Our
Approach: |
| We
have developed a system that can perform dense, fast
range-based tracking with modest computational complexity.
We apply ordered disparity search techniques to prune
most of the disparity search computation during foreground
detection and disparity estimation, yielding a fast,
illumination-insensitive 3D tracking system. When tracking
people, we have found that rendering an orthographic
vertical projection of detected foreground pixels is
a useful model. |
 |
A ”plan view” image facilitates correspondence
in time since only 2D search is required. Previous
systems would segment foreground data into regions
prior to projecting into a plan-view, followed by
region-level tracking and integration, potentially
leading tosub-optimal segmentation and/or object fragmentation.
Instead, we develop a technique that altogether avoids
any early segmentation of foreground data. We merge
the plan-view images from each view and estimate over
time a set of trajectories that best represents the
integrated foreground density. The figure on the left
shows three people are standing in a room, though
not all are visible to each camera. Foreground points
are projected onto a ground plane. Ground plane points
from all cameras are then superimposed into a single
data set before clustering the points to find person
locations.
|
|
Detecting
locations of users in a room using multiple views
and plan-view integration.
|
| Trajectory
estimation is performed using a... |
| Future
Work: |
|
| Publications: |
| 1.
Trevor Darrell, David Demirdjian, Neal Checka, Pedro
Felzenswalb, Plan-view Trajectory Estimation with Dense
Stereo Background Models, Proceedings of the International
Conference on Computer Vision, 2001 |
|
| Demos: |
| Demo
1 (9.85 MB): Shows server
module detecting locations of people in a room using
multiple views. The posture of a person is color coded
(sitting = red, standing = green). An active camera
tracks the tallest person in the room. |
