Real-time probabilistic tracking of faces in video

Abstract

In this note it is discussed how real-time face detection and tracking in video can be achieved by relying on a Bayesian approach realized in a multi-threaded architecture. To this end we propose a probabilistic interpretation of the output provided by a cascade of AdaBoost classifiers. Results show that such integrated approach is appealing with respect either to robustness and computational efficiency. © 2009 Springer Berlin Heidelberg.