Visual Focus of Attention estimation with unsupervised online learning

In this paper, we propose a new method for estimating the Visual Focus Of Attention (VFOA) in a video stream captured by a single distant camera and showing several persons sitting around table, like in formal meeting or videoconferencing settings. The visual targets for a given person are automatically extracted on-line using an unsupervised algorithm that incrementally learns the different appearance clusters from low-level visual features computed from face patches provided by a face tracker without the need of an intermediate errorprone step of head-pose estimation as in classical approaches. The clusters learnt in that way can then be used to classify the different visual attention targets of the person during a tracking run, without any prior knowledge on the environment and the configuration of the room or the visible persons. Experiments on public datasets containing almost two hours of annotated videos from meetings and video-conferencing show that the proposed algorithm produces state-of-the-art results and even outperforms a traditional supervised method that is based on head orientation estimation and that classifies visual focus of attention using Gaussian Mixture Models.