3D tracking of multiple people using their 2D face locations

Abstract

In this paper, we address tracking of multiple people in complex 3D scenes, using multiple calibrated and synchronized far-field recordings. Our approach utilizes the faces detected in every camera view. Faces of the same person seen from the different cameras are associated by first finding all possible associations and then choosing the best option by means of a 3D stochastic tracker. The performance of the proposed system is evaluated by using the outputs of two grossly different 2D face detectors as input to our 3D algorithm. The multi-camera videos employed come from the CLEAR evaluation campaign. Even though the two 2D face detectors have very different performance, the 3D tracking performance of our system remains practically unchanged. © 2007 International Federation for Information Processing.