Video behaviour mining using a dynamic topic model

International Journal of Computer Vision

Volumn 98, Issue 3, 2012, Pages 303-323

  Hospedales, Timothy ^a   Gong, Shaogang ^a   Xiang, Tao ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Bayesian methods; Behaviour profiling; Learning for vision; Probabilistic modelling; Topic models; Video behaviour mining

Indexed keywords

BAYESIAN METHODS; BEHAVIOUR PROFILING; LEARNING FOR VISION; PROBABILISTIC MODELLING; TOPIC MODEL;

BAYESIAN NETWORKS; INFERENCE ENGINES;

VIDEO RECORDING;

EID: 84867864817     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-011-0510-7     Document Type: Article

References (49)

1. Ali, S., & Shah, M. (2008). Floor fields for tracking in high density crowd scenes. In European conference on computer vision.
2. Basharat, A., Gritai, A., & Shah, M. (2008). Learning object motion patterns for anomaly detection and improved object detection. In IEEE conference on computer vision and pattern recognition.
3. Benezeth, Y., Jodoin, P.-M., Saligrama, V., & Rosenberger, C. (2009). Abnormal events detection based on spatio-temporal cooccurences. In IEEE conference on computer vision and pattern recognition.
4. Berclaz, J., Fleuret, F., & Fua, P. (2008). Multi-camera tracking and atypical motion detection with behavioral maps. In European conference on computer vision.
5. Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.
6. Blei, D., & Lafferty, J. (2006). Dynamic topic models. In International conference on machine learning.
7. Blei, D., & McAuliffe, J. (2007). Supervised topic models. In Neural information processing systems.
8. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
9. Boiman, O., & Irani, M. (2007). Detecting irregularities in images and in video. International Journal of Computer Vision, 74(1), 17-31. (Pubitemid 46673101)
10. Chang, S. F., Luo, J., Maybank, S., Schonfeld, D., & Xu, D. (2008). An introduction to the special issue on event analysis in videos. IEEE Transactions on Circuits and Systems for Video Technology, 18(11), 1469-1472.
11. Chen, M. y., Li, H., & Hauptmann, A. (2009). Informedia @ trecvid 2009: analyzing video motions. In Proc TRECvid.
12. Dee, H., & Hogg, D. (2004). Detecting inexplicable behaviour. In British machine vision conference.
13. Dollar, P., Rabaud, V., Cottrell, G., & Belongie, S. (2005). Behavior recognition via sparse spatio-temporal features. In VS-PETS (pp. 65-72). (Pubitemid 46178025)
14. Duong, T., Bui, H., Phung, D., & Venkatesh, S. (2005). Activity recognition and abnormality detection with the switching hidden semi-Markov model. In IEEE conference on computer vision and pattern recognition.
15. Gilks, W., Richardson, S., & Spiegelhalter, D. (Eds.) (1995). Markov chain Monte Carlo in practice. London/Boca Raton: Chapman & Hall/CRC Press.
16. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101, 5228-5235. (Pubitemid 38469131)
17. Griffiths, T., Steyvers, M., Blei, D., & Tenenbaum, J. (2007). Integrating topics and syntax. In Neural information processing systems.
18. Gruber, A., Rosen-Zvi, M., & Weiss, Y. (2007). Hidden topic Markov models. In Artificial intelligence and statistics.
19. HOSDB. Imagery library for intelligent detection systems (i-lids). In IEEE conf. on crime and security (2006).
20. Hospedales, T., Gong, S., & Xiang, T. (2009). A Markov clustering topic model for behaviour mining in video. In IEEE international conference on computer vision.
21. Hu, W., Tan, T., Wang, L., & Maybank, S. (2004). A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews, 34(3), 334-352.
22. Hu,W., Xiao, X., Fu, Z., Xie, D., Tan, T., &Maybank, S. (2006). A system for learning statistical motion patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9), 1450-1464. (Pubitemid 44132651)
23. Hu, Z., Ye, G., Jia, G., Chen, X., Hu, Q., Jiang, K.,Wang, Y., Qing, L., Tian, Y., Wu, X., & Gaoa, W. (2009). Pku@trecvid2009: Singleactor and pair-activity event detection in surveillance video. In Proc. TRECvid.
24. Inoue, N., Hao, S., Saito, T., & Shinoda, K. (2009). Titgt at trecvid 2009 workshop. In Proc. TRECvid.
25. Johnson, N., & Hogg, D. (1996). Learning the distribution of object trajectories for event recognition. Image and Vision Computing, 8, 609-615. (Pubitemid 126360016)
26. Kapoor, A., Horvitz, E., & Basu, S. (2007). Selective supervision: Guiding supervised learning with decision-theoretic active learning. In International joint conference on artificial intelligence.
27. Kim, J., & Grauman, K. (2009). Observe locally, infer globally: a space-time mrf for detecting abnormal activities with incremental update. In IEEE conference on computer vision and pattern recognition.
28. Li, J., Gong, S., & Xiang, T. (2008). Global behaviour inference using probabilistic latent semantic analysis. In British machine vision conference.
29. Meng, J., & Chang, S.-F. (1996). Tools for compressed-domain video indexing and editing. In SPIE conference on storage and retrieval for image and video databases.
30. National institute of standards and technology (NIST): Trec video retrieval evaluation. http://trecvid.nist.gov/.
31. 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.
32. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
33. Pritch, Y., Rav-Acha, A., & Peleg, S. (2008). Nonchronological video synopsis and indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11), 1971-1984.
34. Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. (2004). The author-topic model for authors and documents. In Uncertainty in artificial intelligence.
35. Saleemi, I., Shafique, K., & Shah, M. (2009). Probabilistic modeling of scene dynamics for applications in visual surveillance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(8), 1472-1485.
36. Scovanner, P., Ali, S.,& Shah, M. (2007). A 3-dimensional sift descriptor and its application to action recognition. In ACM international conference on multimedia.
37. Sillito, R. R., & Fisher, R. B. (2008). Semi-supervised learning for anomalous trajectory detection. In British machine vision conference.
38. Smith, K., Quelhas, P., & Gatica-Perez, D. (2006). Detecting abandoned luggage items in a public space. In Performance evaluation of tracking and surveillance (PETS) workshop.
39. Stauffer, C., & Grimson, W. (2000). Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 747-757.
40. Wallach, H. (2006). Topic modeling: beyond bag-of-words. In International conference on machine learning.
41. Wallach, H., Murray, I., Salakhutdinov, R., & Mimno, D. (2009). Evaluation methods for topic models. In International conference on machine learning.
42. Wang, Y., & Mori, G. (2009). Human action recognition by semilatent topic models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(10), 1762-1774.
43. Wang, X., Tieu, K., & Grimson, E. (2006). Learning semantic scene models by trajectory analysis. In European conference on computer vision.
44. Wang, X., Ma, X., & Grimson, E. (2009). Unsupervised activity perception by hierarchical bayesian models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), 539-555.
45. Xiang, T., & Gong, S. (2006). Beyond tracking: Modelling activity and understanding behaviour. International Journal of Computer Vision, 61(1), 21-51.
46. Xiang, T., & Gong, S. (2008a). Activity based surveillance video content modelling. Pattern Recognition, 41, 2309-2326.
47. Xiang, T., & Gong, S. (2008b). Video behavior profiling for anomaly detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(5), 893-908. (Pubitemid 351486756)
48. Xie, L., Sundaram, H., & Campbell, M. (2008). Event mining in multimedia streams. Proceedings of the IEEE, 96(4), 623-647.
49. Zhong, H., Shi, J., & Visontai,M. (2004). Detecting unusual activity in video. In IEEE conference on computer vision and pattern recognition (pp. 819-826).