Abstract

Recent investigations have shown the advantages of keeping multiple hypotheses during visual tracking.  In this paper we explore an alternative method that keeps just a single hypothesis per tracked object for computational efficiency, but displays robust performance and recovery from error by using segmentation provided by a stereo module.  The method is implemented in the domain of people-tracking, using a novel combination of stereo information for continuous detection and intensity image correlation for tracking.  Real-time stereo provides extended information for 3D detection and tracking, even in the presence of crowded scenes, obscuring objects, and large scale changes.  We are able to reliably detect and track people in natural environments, on an implemented system that runs at more than 10 Hz on standard PC hardware.

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Movies

Five people, random motion	Three people, lingering	Two people passing in hall
Four people, lingering	Five people in succession	One person running

David Beymer  (beymer@ai.sri.com)  /  Aug 27, 1999