Head Pose Estimation in First-Person Camera Views
Abstract: 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.
Citation:
Alletto, Stefano; Serra, Giuseppe; Calderara, Simone; Cucchiara, Rita "Head Pose Estimation in First-Person Camera Views" International Conference on Pattern Recognition, Stockholm, Sweden, pp. 4188 -4193 , 24-28 Aug. 2014, 2014 DOI: 10.1109/ICPR.2014.718
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- DOI: 10.1109/ICPR.2014.718