The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.11 - Nov. (2013 vol.35)
pp: 2803-2809
Minyoung Kim , Dept. of Electron. & IT Media Eng., Seoul Nat. Univ. of Sci. & Technol., Seoul, South Korea
ABSTRACT
We consider the multiple time-series alignment problem, typically focusing on the task of synchronizing multiple motion videos of the same kind of human activity. Finding an optimal global alignment of multiple sequences is infeasible, while there have been several approximate solutions, including iterative pairwise warping algorithms and variants of hidden Markov models. In this paper, we propose a novel probabilistic model that represents the conditional densities of the latent target sequences which are aligned with the given observed sequences through the hidden alignment variables. By imposing certain constraints on the target sequences at the learning stage, we have a sensible model for multiple alignments that can be learned very efficiently by the EM algorithm. Compared to existing methods, our approach yields more accurate alignment while being more robust to local optima and initial configurations. We demonstrate its efficacy on both synthetic and real-world motion videos including facial emotions and human activities.
INDEX TERMS
Hidden Markov models, Videos, Heuristic algorithms, Optimization, Biological system modeling, Probabilistic logic, Inference algorithms,probabilistic models, Conditional random fields, sequence alignment, dynamic time warping
CITATION
Minyoung Kim, "Conditional Alignment Random Fields for Multiple Motion Sequence Alignment", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 11, pp. 2803-2809, Nov. 2013, doi:10.1109/TPAMI.2013.95
REFERENCES
[1] A. Baak, T. Helten, G. Pons-Moll, M. Müller, H.-P. Seidel, and B. Rosenhahn, "Analyzing and Evaluating Markerless Motion Tracking Using Inertial Sensors," Proc. European Conf. Computer Vision, Sept. 2010.
[2] A. Baak, M. Müller, and H.-P. Seidel, "An Efficient Algorithm for Keyframe-Based Motion Retrieval in the Presence of Temporal Deformations," Proc. ACM Int'l Conf. Multimedia Information Retrieval, 2008.
[3] G.J. Barton and M.J. Sternberg, "A Strategy for the Rapid Multiple Alignment of Protein Sequences," J. Molecular Biology, vol. 198, no. 2, pp. 327-337, 1987.
[4] D.J. Berndt and J. Clifford, "Using Dynamic Time Warping to Find Patterns in Time Series," Proc. AAAI Workshop Knowledge Discovery in Databases, 1994.
[5] G. Blackshields, I.M. Wallace, M. Larkin, and D.G. Higgins, "Analysis and Comparison of Benchmarks for Multiple Sequence Alignment," Silico Biology, vol. 6, no. 4, pp. 321-339, 2006.
[6] R. Durbin, S.R. Eddy, A. Krogh, and G. Mitchison, Biological Sequence Analysis. Cambridge Univ. Press, 1998.
[7] S.R. Eddy, "Profile Hidden Markov Models," Bioinformatics, vol. 14, no. 9, pp. 755-763, 1998.
[8] M. Kim and V. Pavlovic, "Discriminative Learning for Dynamic State Prediction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1847-1861, Oct. 2009.
[9] J. Lafferty, A. McCallum, and F. Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data," Proc. Int'l Conf. Machine Learning, 2001.
[10] J. Lien, T. Kanade, J. Cohn, and C. Li, "Detection, Tracking, and Classification of Action Units in Facial Expression," J. Robotics and Autonomous Systems, 1999.
[11] J. Listgarten, R.M. Neal, S.T. Roweis, and A. Emili, "Multiple Alignment of Continuous Time Series," Proc. Neural Information Processing Systems Conf., vol. 17, 2005.
[12] M. Müller, A. Baak, and H.-P. Seidel, "Efficient and Robust Annotation of Motion Capture Data," Proc. ACM Siggraph/Eurographics Symp. Computer Animation, 2009.
[13] C. Notredame, "Recent Evolutions of Multiple Sequence Alignment Algorithms," PLoS Computational Biology, vol. 3, no. 8, 2007.
[14] G. Pons-Moll, A. Baak, T. Helten, M. Müller, H.-P. Seidel, and B. Rosenhahn, "Multisensor-Fusion for 3D Full-Body Human Motion Capture," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2010.
[15] L.R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286. Feb. 1989,
[16] T.-P. Tian, R. Li, and S. Sclaroff, "Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions," Proc. IEEE Workshop Computer Vision and Pattern Recognition, 2005.
[17] Y. Tian, "Evaluation of Face Resolution for Expression Analysis," Proc. Computer Vision and Pattern Recognition, Workshop Face Processing in Video, 2004.
[18] P. Viola and M. Jones, "Robust Real-Time Object Detection," Int'l J. Computer Vision, vol. 57, no. 2, pp. 137-154, 2001.
[19] P. Yang, Q. Liu, and D.N. Metaxas, "Rankboost with l1 Regularization for Facial Expression Recognition and Intensity Estimation," Proc. IEEE Int'l Conf. Computer Vision, 2009.
70 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool