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An Online HDP Mixture Model for Video Mining

Volume 14, Number 11, November 2018, pp. 2864-2876
DOI: 10.23940/ijpe.18.11.p32.28642876

Lin Tanga, Lin Liub, Mingjing Tangc, and Yu Sunb

aKey Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, 650500, China
bSchool of Information, Yunnan Normal University, Kunming, 650500, China
cPresident Office, Yunnan Normal University, Kunming, 650500, China

(Submitted on August 4, 2018; Revised on September 6, 2018; Accepted on October 6, 2018)

Abstract:

In this paper, we address two problems in video mining: real-time inference and the automatic decision of the number of activities in videos. To solve these problems, we present a real-time Bayesian non-parametric model that is able to discover activities and interactions of videos in real-time. In this model, there are two layers modeled in each scene, which are activities and interactions. An activity is represented as the distribution over visual words, and an interaction is represented as the distribution over activities. Then, the Hierarchical Dirichlet Process (HDP) model connects these two layers of video and automatically decides the number of clusters. Moreover, we developed a hybrid stochastic variational Gibbs sampling algorithm for inferring the parameters of the HDP mixture model. This online inference algorithm has the capacity to process the massive video stream dataset. Finally, the detailed experimental results in a crowded traffic scene and a simulated dataset are described and reveal that our online HDP mixture model achieves superior performance in real-time anomaly activity detection.

 

References: 19

                  1. V. Kaltsa, A. Briassouli, and I. Kompatsiaris, “Multiple Hierarchical Dirichlet Processes for Anomaly Detection in Traffic,” Computer Vision and Image Understanding, Vol. 169, pp. 28-39, 2018
                  2. L. Tang, L. Liu, and J. Su, “Real-Time Video Mining based on SNGRLD-rLDA Model,” in Proceedings of IEEE International Conference on Intelligent Human-Machine Systems & Cybernetics, pp. 159-164, 2014
                  3. D. M. Blei, “Probabilistic Topic Models,” Communications of the ACM, Vol. 55, No. 4, pp. 77-84, 2012
                  4. M. D. Hoffman, D. M. Blei, and F. Bach, “Online Learning for Latent Dirichlet Allocation,” in Proceedings of International Conference on Neural Information Processing Systems, pp. 856-864, Curran Associates Inc, 2010
                  5. Y. W. Teh, M. I. Jordan, and M. J. Beal, “Hierarchical Dirichlet Processes,” Publications of the American Statistical Association, Vol. 101, No. 476, pp. 1566-1581, 2006
                  6. D. Larlus, J. Verbeek, and F. Jurie, “Category Level Object Segmentation by Combining Bag-of-Words Models with Dirichlet Processes and Random Fields,” International Journal of Computer Vision, Vol. 88, No. 2, pp. 238-253, 2010
                  7. X. Wang, X. Ma, and W. E. L. Grimson, “Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models,” IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 31, No. 3, pp. 539-555, 2009
                  8. O. Isupova, D. Kuzin, and L. Mihaylova, “AnomalyDetection in Video with Bayesian Nonparametrics,” in Proceedings of ICML2016 Anomaly Detection Workshop, 2016
                  9. D. Kuettel, M. D. Breitenstein, and L. V. Gool, “What’s Going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes,” in Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1951-1958, IEEE, 2010
                  10. M. Y. Yang, W. Liao, Y. Cao, and B. Rosenhahn, “Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models,” Photogrammetric Engineering & Remote Sensing, Vol. 84, No. 4, 2018
                  11. M. Gasparini, “Markov Chain Monte Carlo in Practice,” Technometrics, Vol. 39, No. 3, pp. 338-338, 1999
                  12. B. Schölkopf, J. Platt, and T. Hofmann, “A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation,” Advances in Neural Information Processing Systems, Vol. 19, 2007
                  13. L. Tang, L. Liu, and J. H. Gan, “A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining,” Algorithms, Vol. 11, No. 7, pp. 97, 2018
                  14. D. Mimno, M. D. Hoffman, and D. M. Blei, “Sparse Stochastic Inference for Latent Dirichlet Allocation,” Vol. 3, pp. 362-365, 2012
                  15. M. Girolami and B. Calderhead, “Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods,” Journal of the Royal Statistical Society, Vol. 73, No. 2, pp. 123-214, 2015
                  16. M. Welling and Y. W. Teh, “Bayesian Learning via Stochastic Gradient Langevin Dynamics,” in Proceedings of International Conference on International Conference on Machine Learning, pp. 681-688, Omnipress, 2011
                  17. S. Patterson and Y. W. Teh, “Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex,” Advances in Neural Information Processing Systems, pp. 3102-3110, 2013
                  18. O. Isupova, D. Kuzin, and L. Mihaylova, “Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 99, pp. 1-14, 2018
                  19. Junction Dataset, (http://www.eecs.qmul.ac.uk/~sgg/QMUL_Junction_Datasets/Junction/Junction.html, last accessed on March 5 2017)

                                   

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