Real-Time Tracking of Multiple People Using Continuous Detection

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 employing continuous detection during tracking. 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. 1 Introduction Our goal is to simulataneously track a number of people in crowded natural environments. A system of this sort would be useful in a number of applic...