Integrated Person Tracking Using Stereo, Color, and Pattern Detection

摘要

We present an approach to real-time person tracking in crowded and/or unknown environments using integration of multiple visual modalities. We combine stereo, color, and face detection modules into a single robust system, and show an initial application in an interactive, face-responsive display. Dense, real-time stereo processing is used to isolate users from other objects and people in the background. Skin-hue classification identifies and tracks likely body parts within the silhouette of a user. Face pattern detection discriminates and localizes the face within the identified body parts. Faces and bodies of users are tracked over several temporal scales: short-term (user stays within the field of view), medium-term (user exits/reenters within minutes), and long term (user returns after hours or days). Short-term tracking is performed using simple region position and size correspondences, while medium and long-term tracking are based on statistics of user appearance. We discuss the failure modes of each individual module, describe our integration method, and report results with the complete system in trials with thousands of users.