More Web Proxy on the site http://driver.im/

Article

An adaptation framework for head-pose classification in dynamic multi-view scenarios

Authors:

Anoop K. Rajagopal,

Ramanathan Subramanian,

Radu L. Vieriu,

Kalpathi Ramakrishnan,

Nicu SebeAuthors Info & Claims

ACCV'12: Proceedings of the 11th Asian conference on Computer Vision - Volume Part II

Pages 652 - 666

https://doi.org/10.1007/978-3-642-37444-9_51

Published: 05 November 2012 Publication History

Abstract

Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper.

References

[1]

Murphy-Chutorian, E., Trivedi, M. M.: Head pose estimation in computer vision: A survey. IEEE PAMI 31, 607-626 (2009)

Digital Library

[2]

Tosato, D., Farenzena, M., Cristani, M., Spera, M., Murino, V.: Multi-class Classification on Riemannian Manifolds for Video Surveillance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 378-391. Springer, Heidelberg (2010)

Digital Library

[3]

Zabulis, X., Sarmis, T., Argyros, A. A.: 3d head pose estimation from multiple distant views. In: BMVC (2009)

[4]

Orozco, J., Gong, S., Xiang, T.: Head pose classification in crowded scenes. In: BMVC, pp. 1-11 (2009)

[5]

Benfold, B., Reid, I.: Unsupervised learning of a scene-specific coarse gaze estimator. In: ICCV (2012)

Digital Library

[6]

Chen, C., Odobez, J. M.: We are not contortionists: coupled adaptive learning for head and body orientation estimation in surveillance video. In: CVPR (2012)

Digital Library

[7]

Voit, M., Stiefelhagen, R.: A system for probabilistic joint 3d head tracking and pose estimation in low-resolution, multi-view environments. In: ICVS, pp. 415-424 (2009)

Digital Library

[8]

Stiefelhagen, R., Bowers, R., Fiscus, J. G. (eds.): RT 2007 and CLEAR 2007. LNCS, vol. 4625. Springer, Heidelberg (2008)

[9]

Pan, S. J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345-1359 (2010)

Digital Library

[10]

Dai, W., Yang, Q., Xue, G. R., Yu, Y.: Boosting for transfer learning. In: ICML, pp. 193-200 (2007)

Digital Library

[11]

HOSDB: Imagery library for intelligent detection systems (i-lids). IEEE Crime and Security (2006)

[12]

Muñoz-Salinas, R., Yeguas-Bolivar, E., Saffiotti, A., Carnicer, R. M.: Multicamera head pose estimation. Mach. Vis. Appl. 23, 479-490 (2012)

Digital Library

[13]

Yang, W., Wang, Y., Mori, G.: Efficient Human Action Detection Using a Transferable Distance Function. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp. 417-426. Springer, Heidelberg (2010)

Digital Library

[14]

Lim, J. J., Salakhutdinov, R., Torralba, A.: Transfer learning by borrowing examples for multiclass object detection. In: NIPS, pp. 118-126 (2011)

[15]

Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In: CVPR (2011)

Digital Library

[16]

Zhang, Y., Yeung, D.Y.: A convex formulation for learning task relationships in multi-task learning. In: UAI, pp. 733-742 (2010)

Digital Library

[17]

Zhang, Y., Yeung, D.Y.: Transfer metric learning by learning task relationships. In: KDD (2010)

Digital Library

[18]

Wang, X., Huang, X., Gao, J., Yang, R.: Illumination and Person-Insensitive Head Pose Estimation Using Distance Metric Learning. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 624-637. Springer, Heidelberg (2008)

Digital Library

[19]

Ricci, E., Odobez, J. M.: Learning large margin likelihoods for realtime head pose tracking. In: ICIP (2009)

Digital Library

[20]

Lanz, O.: Approximate bayesian multibody tracking. IEEE PAMI (2006)

Digital Library

[21]

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886-893 (2005)

Digital Library

[22]

Wang, X., Han, T.X., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: ICCV, pp. 32-39 (2009)

Cited By

Barua HMg TPramanick PSarkar C(2024)Enabling Social Robots to Perceive and Join Socially Interacting Groups Using F-formation: A Comprehensive OverviewACM Transactions on Human-Robot Interaction10.1145/368207213:4(1-48)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3682072
(2017)BibliographyFrontiers of Multimedia Research10.1145/3122865.3122878(315-377)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122878
Chen KCai WShea RHuang CLiu JLeung VHsu C(2017)Cloud gamingFrontiers of Multimedia Research10.1145/3122865.3122877(287-314)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122877
Show More Cited By

Recommendations

Face recognition across pose using view based active appearance models (VBAAMs) on CMU multi-PIE dataset
ICVS'08: Proceedings of the 6th international conference on Computer vision systems

In this paper we address the challenge of performing face recognition of a probe set of non-frontal images by performing automatic pose correction using Active Appearance Models (AAMs) and matching against a set of enrollment gallery of frontal images. ...
Multi-Channel Pose-Aware Convolution Neural Networks for Multi-View Facial Expression Recognition
2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)
Although tremendous strides have been made in facial expression recognition(FER), recognizing facial expressions in non-frontal views remains an open challenge due to the limited access to large scale training data with various poses. To make full use of ...
Pose-Robust Facial Expression Recognition Using View-Based 2D + 3D AAM

This paper proposes a pose-robust face tracking and facial expression recognition method using a view-based 2D 3D active appearance model (AAM) that extends the 2D 3D AAM to the view-based approach, where one independent face model is used for a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

ACCV'12: Proceedings of the 11th Asian conference on Computer Vision - Volume Part II

November 2012

810 pages

ISBN:9783642374432

Editors:
Kyoung Mu Lee
Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Korea
,
Yasuyuki Matsushita
Department of Electrical and Computer Engineering, Microsoft Research Asia, No. 5, Danling st., Haidian district, Beijing, P.R. China
,
James M. Rehg
School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, Atlanta, GA, P.R. China
,
Zhanyi Hu
Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, Beijing, GA, P.R. China

Sponsors

DCC: Daejeon Convention Center
DMCITY: Daejeon Metropolitan City
KTO: Korea Tourism Organization
Kaist
DIME: Daejeon International Marketing Enterprise

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 05 November 2012

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Barua HMg TPramanick PSarkar C(2024)Enabling Social Robots to Perceive and Join Socially Interacting Groups Using F-formation: A Comprehensive OverviewACM Transactions on Human-Robot Interaction10.1145/368207213:4(1-48)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3682072
(2017)BibliographyFrontiers of Multimedia Research10.1145/3122865.3122878(315-377)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122878
Chen KCai WShea RHuang CLiu JLeung VHsu C(2017)Cloud gamingFrontiers of Multimedia Research10.1145/3122865.3122877(287-314)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122877
Hsu CHong HElgamal TNahrstedt KVenkatasubramanian N(2017)Multimedia fog computingFrontiers of Multimedia Research10.1145/3122865.3122876(255-286)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122876
Ramanathan SGilani SSebe N(2017)Utilizing implicit user cues for multimedia analyticsFrontiers of Multimedia Research10.1145/3122865.3122875(219-251)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122875
Rizoiu MLee YMishra SXie L(2017)Hawkes processes for events in social mediaFrontiers of Multimedia Research10.1145/3122865.3122874(191-218)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122874
Singh V(2017)Situation recognition using multimodal dataFrontiers of Multimedia Research10.1145/3122865.3122873(159-189)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122873
Cui P(2017)Social-sensed multimedia computingFrontiers of Multimedia Research10.1145/3122865.3122872(137-157)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122872
Jeǵou H(2017)Efficient similarity searchFrontiers of Multimedia Research10.1145/3122865.3122871(105-134)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122871
Atrey PLathey AYakubu A(2017)Encrypted domain multimedia content analysisFrontiers of Multimedia Research10.1145/3122865.3122870(75-104)Online publication date: 19-Dec-2017
https://dl.acm.org/doi/10.1145/3122865.3122870
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents