Abstract
When interacting with a group of people, a robot requires the ability to compute people’s visual focus of attention in order to regulate the turn-taking, to determine attended objects, as well as to estimate the degree of users’ engagement. This work aims at evaluating the possibility of computing real-time multiple users’ focus of attention by combining a random forest approach for head pose estimation with the user’s head joint tracking. The system has been tested both on single users and on couples of users interacting with a simple scenario designed to guide the user attention towards a specific space region. The aim is to highlight the possible requirements and problems arising when dealing with the presence of multiple users. Results show that while the approach is promising, datasets that are different from the ones available in the literature are required in order to improve performance.
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Notes
- 1.
The OpenNI framework provides a set of open source APIs for writing 3D sensing applications that facilitates communication with low-level devices including vision and audio sensors.
References
Sidobre, D., Broqure, X., Mainprice, J., Burattini, E., Finzi, A., Rossi, S., Staffa, M.: Humanrobot interaction. In: Advanced Bimanual Manipulation. Volume 80 of Springer Tracts in Advanced Robotics. Springer Berlin Heidelberg (2012) 123–172
Staffa, M., De Gregorio, M., Giordano, M., Rossi, S.: Can you follow that guy? In: 22th European Symposium on Artificial Neural Networks, ESANN 2014, 23–25 April 2014, Bruges, Belgium, pp. 511–516 (2014)
Clabaugh, C., Ram, T., Matarić, M.J.: Estimating visual focus of attention in dyadic human-robot interaction for planar tasks. In: International Conference on Social Robotics, October 2015
Burattini, E., Finzi, A., Rossi, S., Staffa, M.: Monitoring strategies for adaptive periodic control in behavior-based robotic systems. In: Advanced Technologies for Enhanced Quality of Life, AT-EQUAL 2009, pp. 130–135, July 2009
Vatahska, T., Bennewitz, M., Behnke, S.: Feature-based head pose estimation from images. In: 7th IEEE-RAS International Conference on Humanoid Robots, pp. 330–335 (2007)
Burattini, E., Finzi, A., Rossi, S., Staffa, M.: Attentional human-robot interaction in simple manipulation tasks. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction. HRI 2012, pp. 129–130. ACM, New York (2012)
Vinciarelli, A., Salamin, H., Polychroniou, A., Mohammadi, G., Origlia, A.: From nonverbal cues to perception: personality and social attractiveness. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds.) Cognitive Behavioural Systems. LNCS, vol. 7403, pp. 60–72. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34584-5_5
Brinck, I.: Joint attention, triangulation and radical interpretation: a problem and its solution. Dialectica 58(2), 179–205 (2004)
Broz, F., Kose-Bagci, H., Nehaniv, C.L., Dautenhahn, K.: Towards automated human-robot mutual gaze. In: Proceedings of International Conference on Advances in Computer-Human Interactions (ACHI) (2011)
Das, D., Rashed, M.G., Kobayashi, Y., Kuno, Y.: Supporting human-robot interaction based on the level of visual focus of attention. IEEE Trans. Hum. Mach. Syst. 45(6), 664–675 (2015)
Nakano, Y.I., Ishii, R.: Estimating user’s engagement from eye-gaze behaviors in human-agent conversations. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 139–148. ACM (2010)
Short, E., Matarić, M.J.: Towards robot moderators: understanding goal-directed multi-party interactions. In: AAAI Fall Symposium on Artificial Intelligence and Human-Robot Interaction, November 2015
Fanelli, G., Weise, T., Gall, J., 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). doi:10.1007/978-3-642-23123-0_11
Kennedy, J., Baxter, P., Belpaeme, T.: Head pose estimation is an inadequate replacement for eye gaze in child-robot interaction. In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts, HRI 2015, pp. 35–36. Extended Abstracts (2015)
Babcock, J.S., Pelz, J.B.: Building a lightweight eyetracking headgear. In: Proceedings of the 2004 Symposium on Eye Tracking Research & Applications, pp. 109–114. ACM (2004)
Balasubramanian, V., Ye, J., Panchanathan, S.: Biased manifold embedding: a framework for person-independent head pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–7 (2007)
Muoz-Salinas, R., Yeguas-Bolivar, E., Saffiotti, A., Medina-Carnicer, R.: Multi-camera head pose estimation. Mach. Vis. Appl. 23(3), 479–490 (2012)
Breitenstein, M., Kuettel, D., Weise, T., Van Gool, L., Pfister, H.: Real-time face pose estimation from single range images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Voit, M., Stiefelhagen, R.: Deducing the visual focus of attention from head pose estimation in dynamic multi-view meeting scenarios. In: Proceedings of the 10th International Conference on Multimodal Interfaces, ICMI 2008, pp. 173–180. ACM (2008)
Stiefelhagen, R., Yang, J., Waibel, A.: Simultaneous tracking of head poses in a panoramic view. In: Proceedings of 15th International Conference on Pattern Recognition, vol. 3, pp. 722–725 (2000)
Johansson, M., Skantze, G., Gustafson, J.: Head pose patterns in multiparty human-robot team-building interactions. In: Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds.) ICSR 2013. LNCS (LNAI), vol. 8239, pp. 351–360. Springer, Heidelberg (2013). doi:10.1007/978-3-319-02675-6_35
Sheikhi, S., Odobez, J.-M.: Recognizing the visual focus of attention for human robot interaction. In: Salah, A.A., Ruiz-del-Solar, J., Meriçli, Ç., Oudeyer, P.-Y. (eds.) HBU 2012. LNCS, vol. 7559, pp. 99–112. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34014-7_9
Fanelli, G., Dantone, M., Gall, J., Fossati, A., Van Gool, L.: Random forests for real time 3D face analysis. Int. J. Comput. Vis. 101(3), 437–458 (2013)
Acknowledgment
The research leading to these results has been supported by the RoDyMan project, which has received funding from the European Research Council FP7 Ideas under Advanced Grant agreement number 320992 and supported by the Italian National Project Security for Smart Cities PON-FSE Campania 2014-20. The authors are solely responsible for the content of this manuscript. The Authors thank Silvano Sorrentino for his contribution in code development.
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Rossi, S., Leone, E., Staffa, M. (2016). Using Random Forests for the Estimation of Multiple Users’ Visual Focus of Attention from Head Pose. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science(), vol 10037. Springer, Cham. https://doi.org/10.1007/978-3-319-49130-1_8
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