Real-time Tracking of Multiple People Using Stereo
David Beyme
Artificial Intelligence Center
SRI International
[Joint work with Kurt Konolige]
Abstract
A fairly standard approach for object tracking uses correlation with a
gray-level template of the object that is recursively updated. Two
difficulties with this approach are object initialization and slow
template drift off the object. We are investigating how adding stereo
input can address these difficulties in a person tracking framework.
First, background subtraction on stereo disparities -- which are less
sensitive than intensities to changes in lighting -- is used to focus
on foreground objects. People are detected in foreground disparity
layers by using correlation with head and torso templates. These same
templates are used again in the tracking module to re-center the person
tracks and avoid template drift. The entire system can track multiple
people in crowded scenes and with large changes in scale. It runs at
around 10 Hz on standard PC hardware. The system has been evaluated on a
number of video clips, some of which have a number of difficult occlusion
events.
Biography
David Beymer is a computer scientist at the Artificial Intelligence
Center of SRI International. He received his Ph.D. in Computer
Science from MIT in 1995, worked in the CS Division of UC Berkeley the
following year as a postdoctoral researcher, and recently spent two
years with the Vision Technology Center of Autodesk. His research
interests are in computer vision and learning, especially face and
body tracking and face recognition.
Eyal Amir
Last modified: Fri May 14 14:07:27 PDT 1999