Multi-camera head pose estimation using an ensemble of exemplars

We present a method for head pose estimation for moving targets in multi-camera environments. Our approach utilizes an ensemble of exemplar classifiers for joint head detection and pose estimation and provides finer-grained predictions than previous approaches. We incorporate dynamic camera selection, which allows a variable number of cameras to be selected at each time step and provides a tunable trade-off between accuracy and speed. On a benchmark dataset for multi-camera head pose estimation, our method predicts head pan angle with a mean absolute error of ~ 8° for different moving targets.