Skip to main content
SpringerLink
Log in

Crowd flux analysis and abnormal event detection in unstructured and structured scenes

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents a spatio-temporal grid-based framework to deal with the complexity of structured and unstructured motion flows that can effectively group optical flows in the field of view into crowds. This approach utilizes motion flows of the features based on a grid in a scene. In order to detect abnormal events in crowded scenes, the proposed method measures motion features including the speed and direction of moving objects based on a spatio-temporal grid-based approach for flow representation. Experiments have been conducted on several different videos in three domains that are crosswalks, escalators, and highways. To evaluate and compare the performance of our method to other methods, ROC curves are plotted which take into consideration both detection rate and false alarm rate for multiple threshold values.

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

Access this article

Log in via an institution

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Andrade EL, Blunsden S, Fisher RB (2006) Hidden markov models for optical flow analysis in crowds. In: Proceedings of the 18th International Conference on Pattern Recognition—volume 01, ICPR ’06. IEEE Computer Society, Washington, DC, pp 460–463. doi:10.1109/ICPR.2006.621

  2. Ali S, Shah M (2007) A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: IEEE computer society conference on computer vision and patten recognition, pp 1–6. doi:ieeecomputersociety.org/10.1109/CVPR.2007.382977

  3. Bertini M, Del Bimbo A, Seidenari L (2012) Scene and crowd behaviour analysis with local space-time descriptors. In: 2012 5th International Symposium on Communications Control and Signal Processing (ISCCSP), pp 1–6. doi:10.1109/ISCCSP.2012.6217857

  4. Cho SH, Nam Y, Hong S, Cho W (2011) Sector based scanning and adaptive active tracking of multiple objects. TIIS 5(6):1166–1191

    Article  Google Scholar 

  5. Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3449–3456. doi:10.1109/CVPR.2011.5995434

  6. Gupta A, Srinivasan P, Shi J, Davis L (2009) Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp 2012–2019. doi:10.1109/CVPR.2009.5206492

  7. Ihaddadene N, Djeraba C (2008) Real-time crowd motion analysis. In: 19th International Conference on Pattern Recognition (ICPR 2008), 8–11 December 2008. IEEE, Tampa, Florida, pp 1–4. doi:10.1109/ICPR.2008.4761041

  8. Li J, Gong S, Xiang T (2008) Global behaviour inference using probabilistic latent semantic analysis. In: BMVC’08, p 10

  9. Lin L, Gong H, Li L, Wang L (2009) Semantic event representation and recognition using syntactic attribute graph grammar. Pattern Recogn Lett 30(2):180–186. Video-based Object and Event Analysis. doi:10.1016/j.patrec.2008.02.023. http://www.sciencedirect.com/science/article/pii/S0167865508000937

  10. Lin L, Lu Y, Pan Y, Chen X (2012) Integrating graph partitioning and matching for trajectory analysis in video surveillance. IEEE Trans Image Process 21(12):4844–4857. doi:10.1109/TIP.2012.2211373

    Article  MathSciNet  Google Scholar 

  11. Liu X, Lin L, Yan S, Jin H, Tao W (2011) Integrating spatio-temporal context with multiview representation for object recognition in visual surveillance. IEEE Trans Circuits Syst Video Technol 21(4):393–407. doi:10.1109/TCSVT.2010.2087570

    Article  Google Scholar 

  12. Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 935–942. doi:ieeecomputersociety.org/10.1109/CVPRW.2009.5206641

  13. Nam Y, Rho S, Park J (2012) Intelligent video surveillance system: 3-tier context-aware surveillance system with metadata. Multimed Tools Appl 57:315–334. doi:10.1007/s11042-010-0677-x

    Article  Google Scholar 

  14. Nam Y, Rho S, Park JH (2012) Inference topology of distributed camera networks with multiple cameras. Multimed Tools Appl 1–21. doi:10.1007/s11042-012-0997-0

  15. Over P, Awad G, Rose RT, Fiscus JG, Kraaij W, Smeaton AF (eds) (2008) TRECVID 2008 workshop participants notebook papers, Gaithersburg, MD, USA, November 2008. National Institute of Standards and Technology (NIST)

  16. Ozturk O, Yamasaki T, Aizawa K (2010) Detecting dominant motion flows in unstructured/structured crowd scenes. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR ’10. IEEE Computer Society, Washington, DC, pp 3533–3536. doi:10.1109/ICPR.2010.862

  17. Sharif M, Ihaddadene N, Djeraba C (2008) Crowd behaviour monitoring on the escalator exits. In: 11th International Conference on Computer and Information Technology, 2008. ICCIT 2008, pp 194–200. doi:10.1109/ICCITECHN.2008.4803051

  18. Tang S, Li J-T, Li M, Xie C, Liu Y, Tao K, Xu S-X (2008) Trecvid 2008 participation by mcg-ict-cas. In: TRECVID’08, pp 1–1

  19. Umn: Unusual Crowd Activity Dataset of University of Minnesota. Umndataset.mha.cs.umn.edu/movies/crowd-activity-all.avi

  20. Wang X, Ma X, Grimson WEL (2009) Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Trans. Pattern Anal Mach Intell 31:539–555. doi:10.1109/TPAMI.2008.87. URL: http://dl.acm.org/citation.cfm?id=1512152.1512378

    Google Scholar 

  21. Xiang T, Gong S (2005) Video behaviour profiling and abnormality detection without manual labelling. In: 10th IEEE International Conference on Computer Vision. ICCV 2005, vol 2, pp 1238–1245. doi:10.1109/ICCV.2005.248

  22. Xiang T, Gong S (2008) Video behavior profiling for anomaly detection. IEEE Trans Pattern Anal Mach Intell 30(5):893–908. doi:10.1109/TPAMI.2007.70731

    Article  Google Scholar 

  23. Zhang D, Gatica-Perez D, Bengio S, McCowan I (2005) Semi-supervised adapted hmms for unusual event detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol 1. IEEE, pp 611–618

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01607, USA

    Yunyoung Nam

Authors
  1. Yunyoung Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunyoung Nam.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nam, Y. Crowd flux analysis and abnormal event detection in unstructured and structured scenes. Multimed Tools Appl 72, 3001–3029 (2014). https://doi.org/10.1007/s11042-013-1593-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1593-7

Keywords

Search

Navigation