Semantic-probabilistic network for video context recognition in video systems

Authors

  • Никита Владиславович Коваленко Odessa National Polytechnic University Shevchenko av., 1, Odessa, Ukraine, 65044, Ukraine
  • Светлана Григорьевна Антощук Odessa National Polytechnic University Shevchenko av., 1, Odessa, Ukraine, 65044, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2013.16386

Keywords:

human behavior, probabilistic models, Bayesian network, ontology, semantic models

Abstract

In the recent years a considerable growth of video surveillance uses can be observed.

Due to the increasing scale and complexity of such systems manual maintenance becomes impossible, which raises a problem of developing automated intelligent surveillance systems. One of the most important tasks solved by surveillance systems is human behavior analysis and recognition, which has many applications from patient state monitoring in medical establishments to suspicious behavior detection and to crime prevention. Analysis shows, that graphical probabilistic models such as Bayesian networks are widely used and are highly effective approach for human behavior recognition.

However, a lack of strict data formalization and structuring makes the task of building a Bayesian network for complex human behavior recognition a highly difficult task. To surpass that limitation, we suggest introducing a domain ontology — a hierarchical decomposition of video contents in the terms of scenarios, situations, object roles and states, which are derived from the low-level features, computed from the annotated ground-truth video data using a set of computer vision methods, and then using this otology as a basis for Bayesian network structure learning.

The performance of the proposed framework was evaluated using a HMDB and a CAVIAR datasets, and we noticed an increased efficiency of human behavior recognition compared to other approaches

Author Biographies

Никита Владиславович Коваленко, Odessa National Polytechnic University Shevchenko av., 1, Odessa, Ukraine, 65044

Postgraduate student

Department  of Information Systems

Светлана Григорьевна Антощук, Odessa National Polytechnic University Shevchenko av., 1, Odessa, Ukraine, 65044

Doctor of Sciences, Professor

Department  of Information Systems

References

  1. Aggarwal, J. Human activity analysis: A review [Текст] / J. Aggarwal, M. Ryoo // ACM Computing Surveys. — 2011. — Vol. 43, Issue 3. — С. 43.
  2. Aggarwal, J. Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities [Текст] / J. Aggarwal, M. Ryoo // In IEEE International Conference on Computer Vision (ICCV). — 2009. — СС. 1593–1600.
  3. Niebles, J. C. Unsupervised learning of human action categories using spatial-temporal words [Текст] / J.C. Niebles, H. Wang, L. Fei-Fei // International Journal of Computer Vision (IJCV). — 2008. — Vol. 79, Issue 3. — СС. 299–318.
  4. Gupta, A. Objects in action: An approach for combining action understanding and object perception [Текст] / A. Gupta, L.S. Davis // In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). — 2007. — СС. 1–8.
  5. Filipovych, R. Recognizing primitive interactions by exploring actor-object states [Текст] / R. Filipovych, E. Ribeiro // IEEE Conference on Computer Vision and Pattern Recognition. — 2008. — СС. 1–7.
  6. Damen, D. Recognizing linked events: Searching the space of feasible explanations [Текст] / D. Damen, D. Hogg // IEEE Conference on Computer Vision and Pattern Recognition. — 2009. — СС. 927–934.
  7. Joo, S.-W. Attribute Grammar-Based Event Recognition and Anomaly Detection [Текст] / S.-W. Joo, R. Chelappa // Conference on Computer Vision and Pattern Recognition Workshop. — 2006. — СС. 107–107.
  8. Gupta, A. Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos [Текст] / A. Gupta, P. Srinivasan, J. Shi, L.S. Davis // In IEEE Conference on Computer Vision and Pattern Recognition. — 2009. — СС. 2012–2019.
  9. Malgireddy, M.R. A temporal Bayesian model for classifying, detecting and localizing activities in video sequences [Текст] / M.R. Malgireddy, I. Inwogu, V. Govindaraju // Computer Vision and Pattern Recognition Workshops. — 2012. — СС. 43–48.
  10. Cooper, G. A Bayesian method for the induction of probabilistic networks from data [Текст] / G. Cooper, E. Herskovits // Machine Learning. — 1992. — Vol. 9. — СС. 309–347.
  11. Murphy, K. Dynamic Bayesian Networks: Representation, Inference and Learning [Текст] / K. Murphy // PhD thesis, University of California at Berkley. — 2002.
  12. HMDB: A Large Video Database for Human Motion Recognition [электронный ресурс] / SERRE LAB. A Brown University Research Group. — Режим доступа : WWW/ URL: http://serre-lab.clps.brown.edu/resources/HMDB/ — 2011. — Загл. с экрана.
  13. CAVIAR Test Case Scenarios [электронный ресурс] / INRIA Labs at Grenoble, France. — Режим доступа : WWW/ URL: http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/ — 2004. — Загл. с экрана.
  14. Aggarwal, J., Ryoo, M. (2011). Human activity analysis: A review. ACM Computing Surveys, 43 (3), 43.
  15. Aggarwal, J., Ryoo, M. (2009). Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In IEEE International Conference on Computer Vision (ICCV), 1593–1600.
  16. Niebles, J. C., Wang, H., Fei-Fei, L. (2008). Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision (IJCV), 79 (3), 299-318.
  17. Gupta, A., Davis, L.S. (2007). Objects in action: An approach for combining action understanding and object perception. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–8.
  18. Filipovych, R., Ribeiro, E. (2008). Recognizing primitive interactions
  19. by exploring actor-object states. IEEE Conference on Computer Vision and Pattern Recognition, 1–7.
  20. Damen, D., Hogg, D. (2009). Recognizing linked events: Searching the space of feasible explanations. IEEE Conference on Computer Vision and Pattern Recognition, 927–934.
  21. Joo, S.-W., Chelappa, R. (2006). Attribute Grammar-Based Event Recognition and Anomaly Detection. Conference on Computer Vision and Pattern Recognition Workshop, 107–107.
  22. Gupta, A., Srinivasan, P., Shi, J. & Davis, L.S. (2009). Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In IEEE Conference on Computer Vision and Pattern Recognition, 2012–2019.
  23. Malgireddy, M.R., Inwogu, I., Govindaraju, V. (2012). A temporal Bayesian model for classifying, detecting and localizing activities in video sequences. Computer Vision and Pattern Recognition Workshops, 43–48.
  24. Cooper, G., Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309–347.
  25. Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California at Berkley.
  26. HMDB: A Large Video Database for Human Motion Recognition. (2011). Retrieved from http://serre-lab.clps.brown.edu/resources/HMDB/.
  27. CAVIAR Test Case Scenarios. (2004). INRIA Labs at Grenoble, France. Retrieved from http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1.

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Published

2013-08-15

How to Cite

Коваленко, Н. В., & Антощук, С. Г. (2013). Semantic-probabilistic network for video context recognition in video systems. Eastern-European Journal of Enterprise Technologies, 4(9(64), 15–18. https://doi.org/10.15587/1729-4061.2013.16386

Issue

Vol. 4 No. 9(64) (2013): Information and control systems

Section

Information and controlling system

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Copyright (c) 2014 Никита Владиславович Коваленко, Светлана Григорьевна Антощук

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