Skip to main content
Log in

Abstract

This article proposes a new methodology of unsupervised event prediction from videos. Detecting events from videos without prior information is a challenging task, as there are no well-accepted definitions about events in a video. It is commonly known that the presence of moving elements in a video scene could be considered as part of an event. The possibility of an event occurring becomes higher if there is an abrupt change in the motion patterns of different object(s) present in that video scene. In this paper, we defined a method to model this phenomenon of object motion. Since we have not considered any prior information while modeling it, the initial event and nonevent classification is carried out with rough set-based approximations, namely positive, boundary, and negative, in the incomplete knowledge base, resulting in an event-nonevent rough sets. We generate three regions with rough sets. Negative class labels are assigned for static objects and those moving with predictable paths. The objects with a huge change in motion are labeled to be positive events. The remaining objects are kept in the boundary region. However, if there is a gradual change in the motion pattern, there arises some possibility of an event occurring. To define the terms, like possible events and must be event, we have fuzzified the boundary region of event-nonevent rough set and assigned different degrees of possibility of an event to occur if there is a change in motion patterns in the trajectory of the objects. That is, the event, nonevent regions are classified with rough sets, and the boundary region is fuzzified with fuzzy sets. We have validated this newly defined event-nonevent rough-fuzzy sets with experimental demonstrations where the proposed method successfully predicted the events to occur in video sequences.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127

    Article  MathSciNet  MATH  Google Scholar 

  2. Pal SK, Meher SK (2013) Natural computing: a problem solving paradigm with granular information processing. Appl Soft Comput 13(9):3944–3955

    Article  Google Scholar 

  3. Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspective and challenges. IEEE Trans Cybern 43(6):1977–1989

    Article  Google Scholar 

  4. Chakraborty DB, Pal SK (2016) Neighborhood granules and rough rule base in tracking. Nat Comput, Springer 15(2):359–370

    Article  MathSciNet  MATH  Google Scholar 

  5. Pawlak Z (1992) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Norwell, MA

    MATH  Google Scholar 

  6. Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17(2–3):191–209

    Article  MATH  Google Scholar 

  7. Yao YY (1997) Combination of rough and fuzzy sets based on-level sets. In: Rough Sets and Data Mining. Springer pp. 301–321

  8. Lingras P (2001) Fuzzy-rough and rough-fuzzy serial combinations in neurocomputing. Neurocomputing 36(1–4):29–44

    Article  MATH  Google Scholar 

  9. Cornelis C, Cock MD, Kerre EE (2003) Intuitionistic fuzzy rough sets: at the crossroads of imperfect knowledge. Expert Syst J Knowl Eng 20(5):260–270

    Article  Google Scholar 

  10. Sen D, Pal SK (2009) Generalized rough sets, entropy, and image ambiguity measures. IEEE Trans Syst, Man, Cybern, Part B 39(4):117–128

    Article  Google Scholar 

  11. Chen YHPSS (2015) Bipolar-valued rough fuzzy set and its applications to the decision information system. IEEE Trans Fuzzy Syst 64:2358–2370

    Google Scholar 

  12. Ray SS, Ganivada A, Pal SK (2015) A granular self-organizing map for clustering and gene selection in microarray data. IEEE Trans Neural Netw Learn Syst 27(9):1890–1906

    Article  MathSciNet  Google Scholar 

  13. Chakraborty DB, Pal SK (2018) Neighborhood rough filter and intuitionistic entropy in unsupervised tracking. IEEE Trans Fuzzy Syst 85:2188–2200

    Article  Google Scholar 

  14. Borges P, Conci N, Cavallaro A (2013) Video-based human behavior understanding: a survey. IEEE Trans CSVT 23(11):1993–2008

    Google Scholar 

  15. Brand M (1996) Understanding manipulation in video. In: Proceedings of the Second Intl Conf on AFGR. IEEE, Killington, VT, pp. 94–99

  16. Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans PAMI 22:747–757

    Article  Google Scholar 

  17. Yang Y, Liu J, Shah M (2009) Video Scene Understanding Using Multi-scale Analysis. In: IEEE Intl Conf on Comp Vision. Kyoto, pp. 1669–1676

  18. Liu KY, Zang T, Wang L (2010) A new parallel video understanding and retrieval system. In: IEEE ICME. Suntec City, pp. 679–684

  19. Gupta A, Srinivasan P, Shi J, Davis L (2009) Unsupervised Video Understanding by Reconciliation of Posture Similarities. In: IEEE CVPR. Miami, FL, pp. 2012–2019

  20. Zaidenberg S, Boulay B, Bremond F (2013) A Generic Framework for Video Understanding Applied to Group Behavior Recognition. In: IEEE AVSS. Beijing, pp. 136–142

  21. Lu C, Shi J, Jia J (2013) Abnormal Event Detection at 150 FPS in Matlab. In: International conference on computer vision, (ICCV) IEEE

  22. Song W, Hagras H (2017) A type-2 fuzzy logic system for event detection in soccer videos. In: 2017 IEEE International conference on fuzzy systems (FUZZ-IEEE), pp. 1–6

  23. Schmid NCKAC (2018) Learning from web videos for event classification. IEEE Trans Circuits Syst Video Technol 58:3019–3029

    Google Scholar 

  24. Milbich T, Bautista M, Sutter E, Ommer B (2017) Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In: IEEE ICCV. Venice, pp. 4404–4414

  25. Cai M, Lu F, Gao Y (2019) Desktop action recognition from first-person point-of-view. IEEE Trans Cybern 49(5):1–13

    Article  Google Scholar 

  26. Yan R, Tang J, Shu X, Li Z, Tian Q (2018) Participation-Contributed Temporal Dynamic Model for Group Activity Recognition. Association for computing machinery, New York, pp. 1292–1300

  27. Neumann L, Zisserman A, Vedaldi A (2019) Future Event Prediction: If and When. In: IEEE CVPR Workshops

  28. Miech A, Laptev I, Sivic J, Wang H, Torresani L, Tran D (2019) Leveraging the Present to Anticipate the Future in Videos. In: IEEE CVPR Workshops

  29. Liang J, Jiang L, Niebles JC, Hauptmann AG (2019) L. Fei-Fei, Peeking into the Future: Predicting Future Person Activities and Locations in Videos. In: IEEE Proc. on CVPR

  30. Lei J, Yu L, Berg T, Bansal M (2020) What is More Likely to Happen Next? Video-and-Language Future Event Prediction. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp. 8769–8784

  31. Yan R, Xie L, Tang J, Shu X, Tian Q (2020) Higcin: hierarchical graph-based cross inference network for group activity recognition. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2020.3034233

    Article  Google Scholar 

  32. Chebi H, Benaissa A (2021) Novel approach by fuzzy logic to deal with dynamic analysis of shadow elimination and occlusion detection in video sequences of high-density scenes. IETE J Res 22:1–12

    Article  Google Scholar 

  33. Chakraborty DB, Pal SK (2021) Rough video conceptualization for real-time event precognition with motion entropy. Inf Sci 543:488–503

    Article  Google Scholar 

  34. Possegger H, Sternig S, Mauthner T, Roth PM, Bischof H (2013) Robust Real-Time Tracking of Multiple Objects by Volumetric Mass Densities. In: IEEE Proc. on CVPR

  35. PETS-2015 (2015) Dataset Released in IEEE Int. WS Perfor. Evaluation of tracking and surveillance

  36. Velastin SA, Gómez-Lira DA (2017) People Detection and Pose Classification Inside a Moving Train Using Computer Vision. In: International visual informatics conference. Springer, pp. 319–330

  37. Mitchell TM (1997) Machine learning. McGraw-Hill, New York

    MATH  Google Scholar 

  38. PETS-2001 (2001) Datasets Realsed In IEEE Int. WS Perfor. Evaluation of tracking and surveillance

  39. AVSS-2007 (2007) Datasets Released In Fourth IEEE Int. Conf Adv Video and signal based surveillance

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Debarati B. Chakraborty.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakraborty, D.B., Yao, J. Event prediction with rough-fuzzy sets. Pattern Anal Applic 26, 691–701 (2023). https://doi.org/10.1007/s10044-022-01119-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-022-01119-7

Keywords

Navigation