Abstract
The paper presents an evaluation of both head pose and visual focus of attention (VFOA) estimation algorithms in a meeting room environment. Head orientation is estimated using a Rao-Blackwellized mixed state particle filter to achieve joint head localization and pose estimation. The output of this tracker is exploited in an Hidden Markov Model (HMM) to estimate people’s VFOA. Contrarily to previous studies on the topic, in our set-up, the potential VFOA of people is not restricted to other meeting participants only, but includes environmental targets (table, slide screen), which renders the task more difficult due to more ambiguity between VFOA target directions. By relying on a corpus of 8 meetings of 8 minutes on average featuring 4 persons involved in the discussion of statements projected on a slide screen, and for which head orientation ground truth was obtained using magnetic sensor devices, we thoroughly assess the performance of the above algorithms, demonstrating the validity of our approaches and pointing out to further research directions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ba, S.O., Odobez, J.M.: A rao-blackwellized mixed state particle filter for head pose tracking. In: ACM-ICMI Workshop on Multi-modal Multi-party Meeting Processing (MMMP), Trento Italy, pp. 9–16. ACM, New York (2005)
Brown, L., Tian, Y.: A study of coarse head pose estimation. In: IEEE Workshop on Motion and Video Computing (Dec. 2002)
Cootes, T., Kittipanya-ngam, P.: Comparing variations on the active appearance model algorithm. In: BMVC (2002)
Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. In: Statistics and Computing (2000)
Lu, L., Zhang, Z., Shum, H., Liu, Z., Chen, H.: Model and exemplar-based robust head pose tracking under occlusion and varying expression. In: CVPR (Dec. 2001)
McGrath, J.: Groups: Interaction and performance. Prentice-Hall, Englewood Cliffs (1984)
Odobez, J.-M.: Focus of attention coding guidelines. Technical Report 2, IDIAP-COM (Jan. 2006)
Otsuka, K., Takemae, Y., Yamato, J., Murase, H.: A probabilistic inference of multiparty-conversation structure based on markov-switching models of gaze patterns, head directions, and utterances. In: Proc. of International Conference on Multimodal Interface (ICMI’05), Trento, Italy, pp. 191–198 (Oct. 2005)
Parker, K.: Speaking turns in small group interaction: a context sensitive event sequence model. Journal of Personality and Social Psychology (1988)
Rae, R., Ritter, H.: Recognition of human head orientation based on artificial neural networks. IEEE Trans. on Neural Network (March 1998)
Stiefelhagen, R., Yang, J., Waibel, A.: Modeling focus of attention for meeting indexing based on multiple cues. IEEE Transactions on Neural Networks 13(4) (2002)
Toyama, K., Blake, A.: Probabilistic tracking in metric space. In: ICCV (Dec. 2001)
Waibel, A., Bett, M., Metze, F., Ries, K., Schaaf, T., Schultz, T., Soltau, H., Yu, H., Zechner, K.: Advances in automatic meeting record creation and access. In: Proc. ICASSP (May 2001)
Wang, P., Ji, Q.: Multi-view face tracking with factorial and switching hmm. In: Workshops on Application of Computer Vision (WACV/MOTION’05), Breckenridge, Colorado (2005)
Wu, Y., Toyama, K.: Wide range illumination insensitive head orientation estimation. In: IEEE Conf. on Automatic Face and Gesture Recognition (April 2001)
Yang, J., Lu, W., Weibel, A.: Skin color modeling and adaptation. In: ACCV (Oct. 1998)
Zhao, L., Pingali, G., Carlbom, I.: Real-time head orientation estimation using neural networks. In: Proc. of ICIP (Sept. 2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Ba, S.O., Odobez, JM. (2007). Head Pose Tracking and Focus of Attention Recognition Algorithms in Meeting Rooms. In: Stiefelhagen, R., Garofolo, J. (eds) Multimodal Technologies for Perception of Humans. CLEAR 2006. Lecture Notes in Computer Science, vol 4122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69568-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-69568-4_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69567-7
Online ISBN: 978-3-540-69568-4
eBook Packages: Computer ScienceComputer Science (R0)