Head Pose Estimation in First-Person Camera Views

• Published in: International Conference on Pattern Recognition pp. 4188 - 4193
• Main Authors: Alletto, Stefano, Serra, Giuseppe, Calderara, Simone, Cucchiara, Rita
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2014
• Subjects: Cameras; Estimation; Face; Hidden Markov models; Real-time systems; Support vector machines
• ISSN: 1051-4651
• DOI: 10.1109/ICPR.2014.718

In this paper we present a new method for head pose real-time estimation in ego-vision scenarios that is a key step in the understanding of social interactions. In order to robustly detect head under changing aspect ratio, scale and orientation we use and extend the Hough-Based Tracker which allows to follow simultaneously each subject in the scene. In an ego-vision scenario where a group interacts in a discussion, each subject's head orientation will be more likely to remain focused for a while on the person who has the floor. In order to encode this behavior we include a stateful Hidden Markov Model technique that enforces the predicted pose with the temporal coherence from a video sequence. We extensively test our approach on several indoor and outdoor ego-vision videos with high illumination variations showing its validity and outperforming other recent related state of the art approaches.