Abstract
In this paper, we present an architecture of a system which aims to personalize the TV content to the viewer reactions. The focus of the paper is on a subset of this system which identifies moments of attentive focus in a non-invasive and continuous way. The attentive focus is used to dynamically improve the user profile by detecting which displayed media or links have drawn the user attention. Our method is based on the detection and estimation of face pose in 3D using a consumer depth camera. Two preliminary experiments were carried out to test the method and to show its link to viewer interest. This study is realized in the scenario of a TV with a second screen interaction (tablet, smartphone), a behaviour that has become common for spectators.
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References
Abe, K., Makikawa, M.: Spatial setting of visual attention and its appearance in head-movement. IFMBE Proceedings 25(4), 1063–1066 (2010)
Aldoma, A.: 3d face detection and pose estimation in pcl (September 2012)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Doshi, A., Trivedi, M.M.: Head and Gaze Dynamics in Visual Attention and Context Learning, pp. 77–84 (2009)
Doshi, A., Trivedi, M.M.: Head and eye gaze dynamics during visual attention shifts in complex environments 12, 1–16 (2012)
Fanelli, G., Dantone, M., Gall, J., Fossati, A., Gool, L.: Random Forests for Real Time 3D Face Analysis. International Journal of Computer Vision 101(3), 437–458 (2012)
Fanelli, G., Dantone, M., Gall, J., Fossati, A., Gool, L.: Random forests for real time 3d face analysis. International Journal of Computer Vision 101, 437–458 (2013)
Fanelli, G., Gall, J., Van Gool, L.: Real time head pose estimation with random regression forests. In: CVPR 2011, pp. 617–624 (June 2011)
Fanelli, G., Gall, J., Van Gool, L.: Real time 3d head pose estimation: Recent achievements and future challenges. In: 2012 5th International Symposium on Communications Control and Signal Processing (ISCCSP), pp. 1–4 (2012)
Fanelli, G., Weise, T., Gall, J., Van Gool, L.: Real time head pose estimation from consumer depth cameras. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 101–110. Springer, Heidelberg (2011)
Fielding, R.T., Taylor, R.N.: Principled design of the modern web architecture. ACM Trans. Internet Technol. 2(2), 115–150 (2002)
Gaschler, A., Jentzsch, S., Giuliani, M., Huth, K., de Ruiter, J., Knoll, A.: Social behavior recognition using body posture and head pose for human-robot interaction. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2128–2133 (October 2012)
Henderson, J.: Regarding scenes. Current Directions in Psychological Science 16, 219–222 (2007)
Kliegr, T., Kuchar, J.: Gain: Analysis of implicit feedback on semantically annotated content. In: WIKT 2012, pp. 75–78 (2012)
Khan, A.Z., Blohm, G., McPeek, R.M., Lefèvre, P.: Differential influence of attention on gaze and head movements. Journal of Neurophysiology 101(1), 198–206 (2009)
Leroy, J., Rocca, F., Mancas, M., Gosselin, B.: Second screen interaction: An approach to infer tv watcher’s interest using 3d head pose estimation. In: Proceedings of the 22nd International Conference on World Wide Web Companion, WWW 2013 Companion, pp. 465–468. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2013)
Microsoft. Kinect sensor, http://www.xbox.com/kinect
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(4), 607–626 (2009)
OpenNI. Open natural interfaces, http://www.openni.org/
PCL. Point cloud library http://pointclouds.org/
PrimeSense. Capri sensor, http://www.primesense.com/news/primesense-unveils-capri
Riche, N., Mancas, M., Duvinage, M., Gosselin, B., Dutoit, T.: Rare2012: A multi-scale rarity-based saliency detection with its comparative statistical analysis. In: Signal Processing: Image Communication (2013)
Seeingmachine. Facelab5, http://www.seeingmachines.com/product/facelab/
Vinciarelli, A., Pantic, M., Bourlard, H.: Social signal processing: Survey of an emerging domain. Image and Vision Computing 27(12), 1743–1759 (2009)
Wright, R.D., Ward, L.M.: Orienting of attention. Oxford University Press (2008)
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© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Leroy, J., Rocca, F., Mancaş, M., Gosselin, B. (2013). 3D Head Pose Estimation for TV Setups. In: Mancas, M., d’ Alessandro, N., Siebert, X., Gosselin, B., Valderrama, C., Dutoit, T. (eds) Intelligent Technologies for Interactive Entertainment. INTETAIN 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 124. Springer, Cham. https://doi.org/10.1007/978-3-319-03892-6_7
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DOI: https://doi.org/10.1007/978-3-319-03892-6_7
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