A sequential topic model for mining recurrent activities from long term video logs

International Journal of Computer Vision

Volumn 103, Issue 1, 2013, Pages 100-126

  Varadarajan, Jagannadan ^a,b   Emonet, Rémi ^a,b   Odobez, Jean Marc ^a,b  

^a IDIAP RESEARCH INSTITUTE   (Switzerland)

^b EPFL   (Switzerland)

Author keywords

Activity analysis; Latent sequential patterns; PLSA; Topic models; Unsupervised; Video surveillance

Indexed keywords

ACTIVITY ANALYSIS; PLSA; SEQUENTIAL PATTERNS; TOPIC MODEL; UNSUPERVISED; VIDEO SURVEILLANCE;

ALGORITHMS; BAYESIAN NETWORKS; OPTIMIZATION;

SECURITY SYSTEMS;

EID: 84876932761     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-012-0596-6     Document Type: Article

References (56)

1. Besnerais, G.; Bercher, J.; & Demoment, G. (1999). A new look at entropy for solving linear inverse problems. IEEE Transactions on Information Theory, 45(5), 1565-1578.
2. Blei, D.; & Lafferty, J. (2006). A correlated topic model of science. Annals of Applied Statistics, 1(1), 17-35.
3. Blei, D. M.; & Lafferty, J. D. (2006). Dynamic topic models. In International Conference on Machine Learning (pp. 113-120). New York.
4. Blei, D. M.; Ng, A.; & Jordan, M. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
5. Boiman, O.; & Irani, M. (2007). Detecting irregularities in images and in video. International Journal of Computer Vision, 74(1), 17-31.
6. Bradley, D.; & Bagnell, J. A. D. (2008). Differentiable sparse coding. In Proceedings of Neural Information Processing Systems (p. 22).
7. Chen, S. S.; & Gopalakrishnan, P. S. (1998). Speaker, environment and channel change detection and clustering via the bayesian information criterion. In DARPA Broadcast News Transcription and Understanding, Workshop (pp. 127-132).
8. Chien, J. T.; & Wu, M. S. (2008). Adaptive bayesian latent semantic analysis. IEEE Transactions on Audio, Speech, and Language Processing, 16(1), 198-207.
9. Emonet, R.; Varadarajan, J.; & Odobez, J. M. (2011). Extracting and locating temporal motifs in video scenes using a hierarchical non parametric bayesian model. In IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs.
10. Fablet, R.; & Bouthemy, P. (2000). Statistical motion-based object indexing using optic flow field. (Vol. 4). In IEEE International Conference on Pattern Recognition, ICPR.
11. Faruquie, T. A.; Kalra, P. K.; & Banerjee, S. (2009). Time based activity inference using latent Dirichlet allocation. In British Machine Vision Conference. London.
12. Girolami, M.; & Kabán, A. (2003). On an equivalence between PLSI and LDA. In ACM SIGIR Conference on Research and Development in, Information Retrieval (pp. 433-434).
13. Gohr, A.; Hinneburg, A.; Schult, R.; & Spiliopoulou, M. (2009). Topic evolution in a stream of documents. In SIAM International Conference on Data Mining (pp. 859-870).
14. Gruber, A.; Rosen-Zvi, M.; & Weiss, Y. (2007). Hidden topic Markov model. In International Conference on Artificial Intelligence and Statistics. San Juan, Puerto Rico.
15. Hervieu, A.; Bouthemy, P.; & Cadre, J. P. L. (2008). A statistical video content recognition method using invariant features on object trajectories. IEEE Transactions on Circuits and Systems for Video Technology, 18(11), 1533-1543.
16. Hofmann, T. (2001). Unsupervised learning by probability latent semantic analysis. Machine Learning, 42, 177-196.
17. Hospedales, T.; Gong, S.; & Xiang, T. (2009). A Markov clustering topic model for mining behavior in video. In IEEE International Conference on Computer Vision. Kyoto.
18. Hospedales, T.; Gong, S.; & Xiang, T. (2012). Video behaviour mining using a dynamic topic model. International Journal of Computer Vision, 98(3), 303-323.
19. Hoyer, P. O. (2005). Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5(2), 1457-1470.
20. http://www.idiap.ch/paper/plsm/plsm.html
21. Keogh, E.; Chakrabarti, K.; Pazzani, M.; & Mehrotra, S. (2000). Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Information Systems, 263-286.
22. Kuettel, D.; Breitenstein, M. D.; Gool, L. V.; & Ferrari, V. (2010). What's going on? discovering spatio-temporal dependencies in dynamic scenes. In IEEE Conference on Computer Vision and, Pattern Recognition (pp. 1951-1958).
23. Li, J.; Gong, S.; & Xiang, T. (2008). Global behaviour inference using probabilistic latent semantic analysis. In British Machine Vision Conference.
24. Li, J.; Gong, S.; & Xiang, T. (2009). Discovering multi-camera behaviour correlations for on-the-fly global activity prediction and anomaly detection. In IEEE International Workshop on Visual Surveillance. Kyoto.
25. Luvison, B.; Chateau, T.; Sayed, P.; & Pham, Q. C.; & Laprest, J. T. (2009). An unsupervised learning based approach for unexpected event detection (pp. 506-513). International Conference on Computer Vision Theory and Applications (VISAPP), Lisboa: In.
26. Makris, D.; & Ellis, T. (2003). Automatic learning of an activity-based semantic scene model. IEEE International Conference on Advanced Video and Signal Based Surveillance, 2(1), 183-188.
27. Mueen, A.; Keogh, E.; Zhu, Q.; Cash, S.; & Westover, B. (2009). Exact discovery of time series motifs. In SIAM International Conference on Data Mining (pp. 473-484).
28. Niebles, J. C.; Wang, H.; & Fei-Fei, L. (2008). Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision, 79(3), 299-318.
29. Quelhas, P.; Monay, F.; Odobez, J. M.; Gatica-perez, D.; & Tuytelaars, T. (2007). A thousand words in a scene. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9), 1575-89.
30. Saleemi, I.; Hartung, L.; & Shah, M. (2010). Scene understanding by statistical modeling of motion patterns. In IEEE Conference on Computer Vision and Pattern Recognition.
31. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464.
32. Sivic, J.; Russell, B. C.; Efros, A. A.; Zisserman, A.; & Freeman, W. T. (2005). Discovering object categories in image collections. In IEEE International Conference on Computer Vision.
33. Stauffer, C.; & Grimson, E. (2000). Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 747-757.
34. Tanaka, Y.; Iwamoto, K.; & Uehara, K. (2005). Discovery of time-series motif from multi-dimensional data based on MDL principle. Machine Learning, 58, 269-300.
35. Teh, Y. W.; Jordan, M. I.; Beal, M. J.; & Blei, D. M. (2006). Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476), 1566-1581.
36. Tommasi, C.; & Kanade, T. (1991). Detection and tracking of point features. Tech. rep.; CMU-CS-91-132.
37. Tritschler, A.; & Gopinath, R. (1999). Improved speaker segmentation and segments clustering using the bayesian information criterion. In Sixth European Conference on Speech Communication and Technology.
38. Vasconcelos, N.; & Lippman, A. (2000). Statistical models of video structure for content analysis and characterization. IEEE Transactions on Image Processing, 1, 3-19.
39. Varadarajan, J.; & Odobez, J. (2009). Topic models for scene analysis and abnormality detection. In IEEE International Workshop on Visual Surveillance. Kyoto.
40. Varadarajan, J.; Emonet, R.; & Odobez, J. (2010). Probabilistic latent sequential motifs: Discovering temporal activity patterns in video scenes. In British Machine Vision Conference (pp. 117.1-117.11). Aberystwyth.
41. Varadarajan, J.; Emonet, R.; & Odobez, J. M. (2010). A sparsity constraint for topic models - application to temporal activity mining. In N. I. P. S. Workshop (Ed.), Workshop on Practical Applications of Sparse Modeling: Open Issues and New Directions.
42. Varadarajan, J.; Emonet, R.; & Odobez, J. (2012). Bridging the past, present and future; modeling scene activities from event relationships and global rules. In IEEE Conference on Computer Vision and Pattern Recognition.
43. Wallach, H. M. (2006). Topic modeling: beyond bag-of-words. International Conference on Machine Learning (pp. 977-984). Pittsburgh, PA.
44. Wang, C.; & Blei, D. (2009). Decoupling sparsity and smoothness in the discrete hierarchical Dirichlet process. In Neural Information Processing Systems (pp. 1982-1989).
45. Wang, C.; Blei, D. M.; & Heckerman, D. (2008). Continuous time dynamic topic models. In Conference on Uncertainty in Artificial Intelligence.
46. Wang, X.; Ma, X.; & Grimson, E. L. (2009). Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), 539-555.
47. Wang, X.; & McCallum, A. (2006). Topics over time: A non-Markov continuous-time model of topical trends. Philadelphia: ACM Conference Knowledge Discovery and Data Mining.
48. Wang, X.; Tieu, K.; Grimson, E. L. (2004). Learning semantic scene models by trajectory analysis. In European Conference on Computer Vision, Vol. 14 (pp. 234-778).
49. Wang, Y.; & Mori, G. (2009). Human action recognition by semi-latent topic models. IEEE Transactions on Pattern Analysis and Machine Intelligence Special Issue on Probabilistic Graphical Models in Computer Vision, 31(10), 1762-1774.
50. Williamson, S.; Wang, C.; Heller, K.; & Blei, D. (2009). Focused topic models. In NIPS workshop on Applications for Topic Models: Text and Beyond. Whistler, Canada.
51. Xiang, T.; & Gong, S. (2008). Video behavior profiling for anomaly detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(5), 893-908.
52. Yang, Y.; Liu, J.; & Shah, M. (2009). Video scene understanding using multi-scale analysis. In IEEE International Conference on Computer Vision. Kyoto.
53. Yao, J.; & Odobez, J. M. (2007). Multi-layer background subtraction based on color and texture. In IEEE CVPR International Workshop on Visual Surveillance (pp. 1-8).
54. Yi, Z.; Jeff, S.; & Artur, D. (2010). Learning compressible models. In Proceedings of SIAM Data Mining (SDM) Conference.
55. Zhang, D.; Gatica-Perez, D.; Bengio, S.; McCowan, I.; & Lathoud, G. (2004). Multimodal group action clustering in meetings. In ACM International Conference on Multimedia, Workshop on Video Surveillance and Sensor, Networks.
56. Zhong, H.; Jianbo, S.; & Mirko, V. (2004). Detecting unusual activity in video, Vol. 2. IEEE Conference on Computer Vision and Pattern Recognition (pp. 819-826). Washington, DC.