Efficient person identification using active cameras in a smartroom
Identifying people is an important task in a Smartroom environment. Active cameras are well suited for the task as they provide high resolution images at almost any location in the room. Since active cameras only observe a small part of the field of view they are capable of, it is important to schedule their movement to efficiently use them for face identification. An effective way to schedule cameras would be to always steer them towards persons currently looking at the camera. To realize this, we utilize the headpose estimation component of our smartroom to schedule the active cameras. To overcome the problems associated with evaluating active camera setups, we propose an evaluation methodology that allows for repetition of experiments without invalidating the comparability of the results. The conducted experiments show a significant improvement in the number of face detections in the view of the active cameras utilizing a headpose based scheduling strategy compared to a less dynamic baseline scheduler.