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Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model
December 2011 (vol. 33 no. 12)
pp. 2451-2464
ASCII Text
x 
 
Timothy M. Hospedales, Jian Li, Shaogang Gong, Tao Xiang, "Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2451-2464, December, 2011.
 
 
BibTex
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@article{ 10.1109/TPAMI.2011.81,
author = {Timothy M. Hospedales and Jian Li and Shaogang Gong and Tao Xiang},
title = {Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
number = {12},
issn = {0162-8828},
year = {2011},
pages = {2451-2464},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.81},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model
IS - 12
SN - 0162-8828
SP2451
EP2464
EPD - 2451-2464
A1 - Timothy M. Hospedales,
A1 - Jian Li,
A1 - Shaogang Gong,
A1 - Tao Xiang,
PY - 2011
KW - Probabilistic model
KW - behavior analysis
KW - imbalanced learning
KW - weakly supervised learning
KW - classification
KW - visual surveillance
KW - topic model
KW - Gibbs sampling.
VL - 33
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Timothy M. Hospedales, Queen Mary University of London, London
Jian Li, Queen Mary, University of London, London
Shaogang Gong, Queen Mary, University of London, London
Tao Xiang, Queen Mary, University of London, London
One of the most interesting and desired capabilities for automated video behavior analysis is the identification of rarely occurring and subtle behaviors. This is of practical value because dangerous or illegal activities often have few or possibly only one prior example to learn from and are often subtle. Rare and subtle behavior learning is challenging for two reasons: 1) Contemporary modeling approaches require more data and supervision than may be available and 2) the most interesting and potentially critical rare behaviors are often visually subtle—occurring among more obvious typical behaviors or being defined by only small spatio-temporal deviations from typical behaviors. In this paper, we introduce a novel weakly supervised joint topic model which addresses these issues. Specifically, we introduce a multiclass topic model with partially shared latent structure and associated learning and inference algorithms. These contributions will permit modeling of behaviors from as few as one example, even without localization by the user and when occurring in clutter, and subsequent classification and localization of such behaviors online and in real time. We extensively validate our approach on two standard public-space data sets, where it clearly outperforms a batch of contemporary alternatives.

[1] T. Xiang and S. Gong, "Beyond Tracking: Modelling Activity and Understanding Behaviour," Int'l J. Computer Vision, vol. 61, no. 1, pp. 21-51, 2006.
[2] N. Robertson and I. Reid, "A General Method for Human Activity Recognition in Video," Computer Vision and Image Understanding, vol. 104, no. 2, pp. 232-248, 2006.
[3] X. Wang, X. Ma, and E. Grimson, "Unsupervised Activity Perception by Hierarchical Bayesian Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 539-555, Mar. 2009.
[4] J. Li, S. Gong, and T. Xiang, "Global Behaviour Inference Using Probabilistic Latent Semantic Analysis," Proc. British Machine Vision Conf., 2008.
[5] T. Hospedales, S. Gong, and T. Xiang, "A Markov Clustering Topic Model for Behaviour Mining in Video," Proc. IEEE 12th Int'l Conf. Computer Vision, 2009.
[6] I. Saleemi, K. Shafique, and M. Shah, "Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 8, pp. 1472-1485, Aug. 2009.
[7] N. Johnson and D. Hogg, "Learning the Distribution of Object Trajectories for Event Recognition," Image and Vision Computing, vol. 8, pp. 609-615, 1996.
[8] W. Hu, T. Tan, L. Wang, and S. Maybank, "A Survey on Visual Surveillance of Object Motion and Behaviors," IEEE Trans. Systems, Man, and Cybernetics, Part C: Applications and Rev., vol. 34, no. 3, pp. 334-352, Aug. 2004.
[9] X. Wang, K. Tieu, and E. Grimson, "Learning Semantic Scene Models by Trajectory Analysis," Proc. European Conf. Computer Vision, 2006.
[10] Y. Benezeth, P.-M. Jodoin, V. Saligrama, and C. Rosenberger, "Abnormal Events Detection Based on Spatio-Temporal Co-Occurences," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[11] M.D. Breitenstein, H. Grabner, and L.V. Gool, "Hunting Nessie—Realtime Abnormality Detection from Webcams," Proc. IEEE Int'l Workshop Visual Surveillance, 2009.
[12] D. Kuettel, M. Breitenstein, L. Van Gool, and V. Ferrari, "What's Going On? Discovering Spatio-Temporal Dependencies in Dynamic Scenes," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[13] R. Mehran, A. Oyama, and M. Shah, "Abnormal Crowd Behavior Detection Using Social Force Model," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[14] Y. Yang, J. Liu, and M. Shah, "Video Scene Understanding Using Multi-Scale Analysis," Proc. IEEE 12th Int'l Conf. Computer Vision, 2009.
[15] I. Saleemi, L. Hartung, and M. Shah, "Scene Understanding by Statistical Modeling of Motion Patterns," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[16] W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan, and S. Maybank, "A System for Learning Statistical Motion Patterns," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1450-1464, Sept. 2006.
[17] J. Berclaz, F. Fleuret, and P. Fua, "Multi-Camera Tracking and Atypical Motion Detection with Behavioral Maps," Proc. European Conf. Computer Vision, 2008.
[18] T. Duong, H. Bui, D. Phung, and S. Venkatesh, "Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[19] T. Xiang and S. Gong, "Video Behavior Profiling for Anomaly Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 5, pp. 893-908, May 2008.
[20] T. Xiang and S. Gong, "Activity Based Surveillance Video Content Modelling," Pattern Recognition, vol. 41, pp. 2309-2326, 2008.
[21] D.M. Blei, A.Y. Ng, and M.I. Jordan, "Latent Dirichlet Allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.
[22] H. He and E. Garcia, "Learning from Imbalanced Data," IEEE Trans. Knowledge and Data Eng., vol. 21, no. 9, pp. 1263-1284, Sept. 2009.
[23] R. Fergus, P. Perona, and A. Zisserman, "Object Class Recognition by Unsupervised Scale-Invariant Learning," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2003.
[24] L. Cao and L. Fei-Fei, "Spatially Coherent Latent Topic Model for Concurrent Segmentation and Classification of Objects and Scenes," Proc. IEEE Int'l Conf. Computer Vision, 2007.
[25] P. Viola, J. Platt, and C. Zhang, "Multiple Instance Boosting for Object Detection," Proc. Neural Information Processing Systems, 2005.
[26] M.H. Nguyen, L. Torresani, F. de la Torre, and C. Rother, "Weakly Supervised Discriminative Localization and Classification: A Joint Learning Process," Proc. IEEE 12th Int'l Conf. Computer Vision, 2009.
[27] X. Wang and E. Grimson, "Spatial Latent Dirichlet Allocation," Proc. Neural Information Processing Systems, 2007.
[28] D. Andrzejewski, A. Mulhern, B. Liblit, and X. Zhu, "Statistical Debugging Using Latent Topic Models," Proc. European Conf. Machine Learning, 2007.
[29] L. Fei-Fei, R. Fergus, and P. Perona, "A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories," Proc. IEEE Ninth Int'l Conf. Computer Vision, 2003.
[30] L. Fei-Fei, R. Fergus, and P. Perona, "One-Shot Learning of Object Categories," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 594-611, Apr. 2006.
[31] S.J. Pan and Q. Yang, "A Survey on Transfer Learning," IEEE Trans. Knowledge and Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
[32] J. Li, S. Gong, and T. Xiang, "Discovering Multi-Camera Behaviour Correlations for On-the-Fly Global Activity Prediction and Anomaly Detection," Proc. IEEE Int'l Workshop Visual Surveillance, 2009.
[33] Markov Chain Monte Carlo in Practice, W. Gilks, S. Richardson, and D. Spiegelhalter, eds. Chapman & Hall, 1995.
[34] H. Wallach, "Topic Modeling: Beyond Bag-of-Words," Proc. 23rd Int'l Conf. Machine Learning, 2006.
[35] C. Andrieu, N.D. Freitas, A. Doucet, and M.I. Jordan, "An Introduction to MCMC for Machine Learning," Machine Learning, vol. 50, pp. 5-43, 2003.
[36] T.P. Minka, "Estimating a Dirichlet Distribution," technical report, Microsoft, 2000.
[37] H. Wallach, I. Murray, R. Salakhutdinov, and D. Mimno, "Evaluation Methods for Topic Models," Proc. Int'l Conf. Machine Learning, 2009.
[38] W. Buntine, "Estimating Likelihoods for Topic Models," Proc. Asian Conf. Machine Learning, 2009.
[39] T.L. Griffiths and M. Steyvers, "Finding Scientific Topics," Proc. Nat'l Academy of Sciences USA, vol. 101, pp. 5228-5235, 2004.
[40] D. Mackay, "Bayesian Interpolation," Neural Computation, vol. 4, no. 3, pp. 415-447, 1991.
[41] D. Blei and J. McAuliffe, "Supervised Topic Models," Proc. Neural Information Processing Systems, 2007.
[42] C. Wang, D. Blei, and F.-F. Li, "Simultaneous Image Classification and Annotation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[43] G. Heinrich, "Parameter Estimation for Text Analysis," technical report, Univ. of Leipzig, 2005.
[44] B. Settles, "Active Learning Literature Survey," Technical Report 1648, Univ. of Wisconsin-Madison, 2009.
[45] C.C. Loy, T. Xiang, and S. Gong, "Stream Based Active Anomaly Detection," Proc. Asian Conf. Computer Vision, 2010.

Index Terms:
Probabilistic model, behavior analysis, imbalanced learning, weakly supervised learning, classification, visual surveillance, topic model, Gibbs sampling.
Citation:
Timothy M. Hospedales, Jian Li, Shaogang Gong, Tao Xiang, "Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2451-2464, Dec. 2011, doi:10.1109/TPAMI.2011.81
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