Context-aware activity recognition and anomaly detection in video

IEEE Journal on Selected Topics in Signal Processing

Volumn 7, Issue 1, 2013, Pages 91-101

  Zhu, Yingying ^a   Nayak, Nandita M ^a   Roy Chowdhury, Amit K ^a  

^a University of California Riverside   (United States)

Author keywords

Context aware activity recognition; context aware anomaly detection; structural model

Indexed keywords

ACTIVITY RECOGNITION; ANOMALY DETECTION; CONTEXT INFORMATION; CONTEXT PATTERNS; CONTEXT-AWARE; CONVEX OPTIMIZATION PROBLEMS; DATA SETS; HIGH FREQUENCY; JOINT MODELING; LEARNED PATTERNS; LEARNING PROCESS; MATHEMATICAL FRAMEWORKS; MODEL PARAMETERS; SPACE AND TIME; SPATIAL AND TEMPORAL DISTRIBUTION; STRUCTURAL MODELS; TRAINING DATA; WIDE AREA;

MODEL STRUCTURES;

CONVEX OPTIMIZATION;

EID: 84873854492     PISSN: 19324553     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSTSP.2012.2234722     Document Type: Article

References (40)

1. C. Schuldt, I. Laptev, and B. Caputo, "Recognizing human actions: A local SVM approach," in Proc. Conf. Pattern Recognition, 2004.
2. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, "Actions as space-time shapes," IEEE Trans. Pattern Anal. and Mach. Intell., vol. 29, no. 12, pp. 2247-2253, Dec. 2007.
3. A. Oliva and A. Torralba, "The role of context in object recognition," Trends in Cognitive Sci., vol. 11, no. 12, pp. 520-527, Dec. 2007.
4. V. I. Morariu and L. S. Davis, "Multi-agent event recognition in structured scenarios," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2011, pp. 3289-3296.
5. B. Yao and L. Fei-Fei, "Modeling mutual context of object and human pose in human object interaction activities," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2010, pp. 17-24.
6. C. H. Teo, Q. Le, A. Smola, and S. V. N. Vishwanathan, "A scalable modular convex solver for regularized risk minimization," in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery and Data Mining, 2007, pp. 727-736.
7. B. Leibe, A. Leonardis, and B. Schiele, "Combined object categorization and segmentation with an implicit shape model," in Proc. Workshop Statist. Learn. in Comput. Vision, ECCV, 2004, pp. 17-32.
8. J. Aggarwal and M. Ryoo, "Human activity analysis: A review," ACM Comput. Surveys, vol. 43, no. 16, Apr. 2011, Article no. 16.
9. M. Ryoo and J. Aggarwal, "Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities," in Proc. IEEE Proc. Conf. Comput. Vision, 2009, pp. 1593-1600.
10. U. Guar, Y. Zhu, B. Song, and A. K. Roy-Chowdhury, "A string of feature graphs model for recognition of complex activities in natural videos," in Proc. IEEE Proc. Conf. Comput. Vision, 2011, pp. 2595-2602.
11. W. Choi, K. Shahid, and S. Savarese, "Learning context for collective activity recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2011.
12. T. Lan, Y. Wang, S. N. Robinovitch, and G. Mori, "Discriminative latentmodels for recognizing contextual group activities," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 8, pp. 1549-1562, Aug. 2012.
13. R. Benmokhtar and I. Laptev, Inria-Willow at Trecvid2010: Surveillance Event Detection, [Online]. Available: http://www-nlpir.nist.gov/projects/tvpubs/ tv10.papers/inria-willow.pdf
14. K. Tang, L. Fei-Fei, and D. Koller, "Learning latent temporal structure for complex event detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2012, pp. 1250-1257.
15. A. Gupta, P. Srinivasan, J. Shi, and L. S. Davis, "Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2009, pp. 2012-2019.
16. Z. Si, M. Pei, B. Yao, and S. Zhu, "Unsupervised learning of event and-or grammar and semantics from video," in Proc. IEEE Conf. Comput. Vis., 2011, pp. 41-48.
17. Y. Benezeth, P.-M. Jodoin, and V. Saligrama, "Abnormality detection using low-level co-occuring events," Pattern Recognition Lett., pp. 423-431, 2011.
18. V. Saligrama, J. Konrad, and P.-M. Jodoin, "Video anomaly identification," IEEE Signal Process. Mag., vol. 27, no. 5, pp. 18-33, Sep. 2010.
19. X.Wang, X. Ma, and W. E. L. Grimson, "Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 3, pp. 539-555, Mar. 2009.
20. V. Saligrama and Z. Chen, "Video anomaly detection based on local statistical aggregates," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2012, pp. 2112-2119.
21. V. Saligrama and Z. Chen, "Sparse reconstruction cost for abnormal events detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2011, pp. 3449-3456.
22. R. Hamid, A. Johnson, S. Batta, A. Bobick, C. Isbell, and G. Coleman, "Detection and explanation of anomalous activities: Representing activities as bags of event -grams," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2005, pp. 1031-1038.
23. R. Hamid, S. Maddi, A. Bobick, and I. Essa, "Unsupervised analysis of activity sequences using event-motifs," in Proc. 4th ACM Int. Workshop Video Surveill. Sens. Netw., 2006.
24. R. Hamid, S. Maddi, A. Bobick, and I. Essa, "Structure fromstatistics -Unsupervised activity analysis using suffix trees," in IEEE Proc. Conf. Comput. Vis., 2007, pp. 1-8.
25. S.-W. Joo and R. Chellappa, "Attribute grammar-based event recognition and anomaly detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2006, p. 107.
26. I. Saleemi, K. Shafique, andM. Shah, "Probabilistic modeling of scene dynamics for applications in visual surveillance," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 8, pp. 1472-1485, Aug. 2009.
27. F. Jiang, Y. Wu, and A. K. Katsaggelos, "A dynamic hierarchical clustering method for trajectory-based unusual video event detection," IEEE Trans. Image Process., vol. 18, no. 4, pp. 907-913, Apr. 2009.
28. Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction," in Proc. Int. Conf. Pattern Recognition, 2004.
29. P. F. Felzenszwalb, R. B. Girshick, and D. McAllester, "Discriminatively Trained Deformable Part Models, Release 4," [Online]. Available: http://people.cs.uchicago.edu/pff/latent-release4/
30. B. Song, T. Jeng, E. Staudt, and A. Roy-Chowdury, "Astochastic graph evolution framework for robust multi-target tracking," in Proc. Euro. Conf. Comput. Vis., 2010.
31. I. Laptev and T. Lindeberg, "Space-time interest points," in Proc. IEEE Proc. Conf. Comput. Vis., 2003, pp. 432-439.
32. Y.-G. J. G.-W. Ngo and J. Yang, "Towards optimal bag of word for object categorization and semantic video retrieval," in Proc. ACM Proc. Conf. Image and Video Retrieval, 2007.
33. C. Desai, D. Ramanan, and C. C. Fowlkes, "Discriminative models for multi-class object layout," Int. J. Comput. Vis., vol. 95, no. 1, pp. 1-12, Oct. 2011.
34. C. M. Bishop, Pattern Recognition and Machine Learning, 2nd ed. New York: Springer, 2006.
35. J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques. Amsterdam, The Netherlands: Elsevier, 2011.
36. F. Jiang, J. Yuan, S. A. Tsaftaris, and A. K. Katsaggelos, "Anomalous video event detection using spatiotemporal context," Comput. Vis. Image Understanding, vol. 115, no. 3, pp. 323-333, Mar. 2011.
37. D. Freedman, R. Pisani, and R. Purves, Statistics, 4th ed. New York: Norton, 2007.
38. S. Oh, A. Hoogs, A. Perera, N. Cuntoor, C.-C. Chen, J. T. Lee, S. Mukherjee, J. Aggarwal, H. Lee, L. Davis, E. Swears, X. Wang, Q. Ji, K. Reddy, M. Shah, C. Vondrick, H. Pirsiavash, D. Ramanan, J. Yuen, A. Torralba, B. Song, A. Fong, A. Roy-Chowdhury, and M. Desai, "A large-scale benchmark dataset for event recognition in surveillance video," in IEEE Conf. Comput. Vis., Pattern Recognition, 2011, pp. 3153-3160.
39. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2005, pp. 886-893.
40. B. Scholkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a high-dimensional distribution," Neural Comput., vol. 13, pp. 1443-1471, 2001.