Skip to main content
SpringerLink
Log in

High Level Video Event Modeling, Recognition and Reasoning via Petri Net

  • Chapter
  • First Online:
Artificial Intelligence and Robotics (ISAIR 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 917))

Included in the following conference series:

  • 721 Accesses

Abstract

A Petri net based framework is proposed for automatic high level video event description, recognition and reasoning purposes. In comparison with the existing approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (ii) more variations of event paths can be found and modeled using the proposed algorithms; (iii) the recognition results are more accurate based on automatic built high level event models. Experimental results show that the proposed method outperforms the existing benchmark in terms of recognition precision and recall. Additional advantages can be achieved such that hidden variations of events hardly identified by humans can also be recognized.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Huimin L, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mobile Networks Appl 23:368–375

    Article  Google Scholar 

  2. Huimin L, Li Y, Shengglin M, Wang D, Kim H, Serikawa S (2018) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J 5(4):2315–2322

    Article  Google Scholar 

  3. Lu H, Wang D, Li Y, Li J, Li X, Kim H, Serikawa S, Humar I (2019) CONet: a cognitive ocean network. IEEE Wirel Commun, In Press

    Google Scholar 

  4. Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50

    Google Scholar 

  5. Huimin L, Li Y, Uemura T, Kim H, Serikawa S (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Gener Comput Syst 82:142–148

    Article  Google Scholar 

  6. Hasan M, Roy-Chowdhury AK (2015) A continuous learning framework for activity recognition using deep hybrid feature models. IEEE Trans Multimedia 17(11):1909–1922

    Article  Google Scholar 

  7. Samanta S, Chanda B (2014) Space-time facet model for human activity classification. IEEE Trans Multimedia 16(6):1525–1535

    Google Scholar 

  8. Wang F, Sun Z, Jiang Y-G, Ngo C-W (2014) Video event detection Using motion relativity and feature selection. IEEE Trans Multimedia 16(5):1303–1315

    Article  Google Scholar 

  9. Cui P, Wang F, Sun L-F, Zhang J-W, Yang S-Q (2012) A matrix-based approach to unsupervised human action categorization. IEEE Trans Multimedia 14(1):102–110

    Article  Google Scholar 

  10. Abbasnejad I, Sridharan S, Denman S, Fookes C, Lucey S (2016) complex event detection using joint max margin and semantic features. In: Proceedings of the international conference on digital image computing—techniques and applications. Gold Coast, QLD, Australia

    Google Scholar 

  11. Veeraraghavan H, Papanikolopoulos NP (2009) Learning to recognize video-based spatiotemporal events. IEEE Trans Intell Transp Syst 10(4):628–638

    Article  Google Scholar 

  12. Kitani KM, Sato Y, Sugimoto A (2005) Deleted Interpolation using a hierarchical bayesian grammar network for recognizing human activity. In: Proceedings of the 2nd joint IEEE international workshop on VS-PETS, pp 239–246. Beijing

    Google Scholar 

  13. Shet VD, Harwood D, Davis LS (2005) VidMAP: video monitoring of activity with prolog. In: Proceedings of IEEE conference on advanced video and signal based surveillance (AVSS), pp 224–229

    Google Scholar 

  14. Song YC, Kautz H, Allen J, Swift M, Li Y, Luo J (2013) A Markov logic framework for recognizing complex events from multimodal data. In: Proceedings of the ACM on international conference on multimodal interaction (ICMI), pp 141–148

    Google Scholar 

  15. Liu L, Wang S, Hu B, Qiong Q, Wen J, Rosenblum DS (2018). Learning structures of interval-based Bayesian networks in probabilistic generative model for human complex activity recognition. Pattern Recogn 81:545–561

    Google Scholar 

  16. Song D, Kim C, Park S-K (2018) A multi-temporal framework for high-level activity analysis: violent event detection in visual surveillance. Inf Sci 447:83–103

    Article  MathSciNet  Google Scholar 

  17. Nawaz F, Janjua NK, Hussain OK (2019) PERCEPTUS: predictive complex event processing and reasoning for IoT-enabled supply chain. Knowl-Based Syst 180:133–146

    Article  Google Scholar 

  18. Skarlatidis A, Artikis A, Filippou J, Paliouras G (2015) A probabilistic logic programming event calculus. Theory Pract Logic Program 15(2):213–245

    Article  Google Scholar 

  19. Cavaliere D, Loia V, Saggese A, Senatore S, Vento M (2019) A human-like description of scene events for a proper UAV-based video content analysis. Knowl-Based Syst 178:163–175

    Article  Google Scholar 

  20. Azorin-Lopez J, Saval-Calvo M, Fuster-Guillo A, Garcia-Rodriguez J (2016) A novel prediction method for early recognition of global human behaviour in image sequences. Neural Process Lett 43:363–387

    Article  Google Scholar 

  21. Castel C, Chaudron L, Tessier (1996). What is going on? A high-level interpretation of a sequence of images. In: Proceedings of the ECCV workshop on conceptual descriptions from images. Cambridge, U.K

    Google Scholar 

  22. Albanese M, Chellappa R, Moscato V, Antonio Picariello VS, Subrahmanian PT, Udrea O (2008) A constrained probabilistic Petri net framework for human activity detection in video. IEEE Trans Multimedia 10(6):982–996

    Article  Google Scholar 

  23. Ghanem N, DeMenthon D, Doermann D, Davis L (2004) Representation and recognition of events in surveillance video using Petri nets. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW’04)

    Google Scholar 

  24. Ghanem N (2007) Petri net models for event recognition in surveillance videos. Doctor thesis, University of Maryland

    Google Scholar 

  25. Lavee G, Rudzsky M, Rivlin E (2013) Propagating certainty in Petri nets for activity recognition. IEEE Trans Circuits Syst Video Technol 23(2):337–348

    Article  Google Scholar 

  26. Lavee G, Borzin A, Rivlin E, Rudzsky M (2007) Building Petri nets from video event ontologies. In: Proceedings of the international symposium on visual computing, Part I, LNCS, vol 4841, pp 442–451

    Google Scholar 

  27. Lavee G, Rudzsky M, Rivlin E, Borzin A (2010) Video event modeling and recognition in generalized stochastic Petri nets. IEEE Trans Circuits Syst Video Technol 20(1):102–118

    Article  Google Scholar 

  28. Borzin A, Rivlin E, Rudzsky M (2007) Surveillance event interpretation using generalized stochastic petri nets. In: Proceedings of the 8th international workshop on image analysis for multimedia interactive services (WIAMIS’07)

    Google Scholar 

  29. Ghrab NB, Boukhriss RR, Fendri E, Hammami M (2018) Abnormal high-level event recognition in parking lot. Adv Intell Syst Comput 736:389–398

    Google Scholar 

  30. Hamidun R, Kordi NE, Endut IR, Ishak SZ, Yusoff MFM (2015) Estimation of illegal crossing accident risk using stochastic petri nets. J Eng Sci Technol 10:81–93

    Google Scholar 

  31. Szwed P (2016) Modeling and recognition of video events with fuzzy semantic petri nets. Skulimowski AMJ, Kacprzyk J (eds) Knowledge, information and creativity support systems: recent trends, advances and solutions, pp 507–518

    Google Scholar 

  32. Szwed P (2014) Video event recognition with fuzzy semantic petri nets. Gruca A et al. (eds) Man-machine interactions, vol 3, pp 431–439

    Google Scholar 

  33. SanMiguel JC, Martínez JM (2012) A semantic-based probabilistic approach for real-time video event recognition. Comput Vis Image Underst 116:937–952

    Article  Google Scholar 

  34. Liu L, Wang S, Guoxin S, Bin H, Peng Y, Xiong Q, Wen J (2017) A framework of mining semantic-based probabilistic event relations for complex activity recognition. Inf Sci 418–419:13–33

    Article  Google Scholar 

  35. Kardas K, Cicekli N (2017) SVAS: surveillance video analysis system. Expert Syst Appl 89:343–361

    Article  Google Scholar 

  36. Acampora G, Foggia P, Saggese A, Vento M (2015) A hierarchical neuro-fuzzy architecture for human behavior analysis. Inf Sci 310:130–148

    Article  Google Scholar 

  37. Caruccio L, Polese G, Tortora G, Iannone D (2019) EDCAR: a knowledge representation framework to enhance automatic video surveillance. Expert Syst Appl 131:190–207

    Article  Google Scholar 

  38. Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE 77(4):541–580

    Article  Google Scholar 

  39. CAVIAR. http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/

  40. VIRAT video dataset release 1.0. http://midas.kitware.com

Download references

Acknowledgements

The work reported in this paper was supported by the National Engineering Laboratory of China for Big Data System Computing Technology. The work reported in this paper was supported in part by the National Natural Science Foundation of China under grant no. 61836005 and no. 61672358, the Natural Science Foundation of Guangdong Province, China, under grant no. 2017A030310521, the Science and Technology Innovation Commission of Shenzhen, China, under grant no. JCYJ20160422151736824.

Author information

Authors and Affiliations

  1. College of Computer Science & Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Zhijiao Xiao, Jianmin Jiang & Zhong Ming

Authors
  1. Zhijiao Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianmin Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhong Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhijiao Xiao .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Xiao, Z., Jiang, J., Ming, Z. (2021). High Level Video Event Modeling, Recognition and Reasoning via Petri Net. In: Lu, H. (eds) Artificial Intelligence and Robotics. ISAIR 2019. Studies in Computational Intelligence, vol 917. Springer, Cham. https://doi.org/10.1007/978-3-030-56178-9_6

Download citation

Publish with us

Policies and ethics

Search

Navigation