Skip to main content
Springer Nature Link
Log in

Crowd Collectiveness Measure via Path Integral Descriptor

  • Conference paper
  • First Online:
Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 662))

Included in the following conference series:

  • 1829 Accesses

Abstract

Crowd collectiveness measuring has attracted a great deal of attentions in recently years. We adopt the path integral descriptor idea to measure the collectiveness of a crowd system. A new path integral descriptor is proposed by exponent generating function to avoid parameter setting. Several good properties of the proposed path integral descriptor are demonstrated in this paper. The proposed path integral descriptor of a set is regard as the collectiveness measure of a set, which can be a moving system such as human crowd, sheep herd and so on. Self-driven particle (SDP) model and the crowd motion database are used to test the ability of the proposed method in measuring collectiveness.

This work is subsidized by National Natural Science Foundation of China under Grant 71673293.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhang, W., Zhao, D., Wang, X.: Agglomerative clustering via maximum incremental path integral. Pattern Recogn. 46(11), 3056–3065 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  2. Moussaid, M., Garnier, S., Theraulaz, G., Helbing, D.: Collective information processing and pattern formation in swarms, flocks, and crowds. Topics Cogn. Sci. 1, 469–497 (2009)

    Article  Google Scholar 

  3. Couzin, I.: Collective cognition in animal groups. Trends Cogn. Sci. 13, 36–43 (2009)

    Article  Google Scholar 

  4. Ballerini, M., et al.: Empirical investigation of starling flocks: a benchmark study in collective animal behaviour. Anim. Behav. 76, 201–215 (2008)

    Article  Google Scholar 

  5. Zhou, B., Wang, X., Tang, X.: Understanding collective crowd behaviors: learning a mixture model of dynamic pedestrian-agents. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition (CVPR 2012) (2012)

    Google Scholar 

  6. Lin, D., Grimson, E., Fisher, J.: Learning visual flows: a lie algebraic approach. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition (CVPR) (2009)

    Google Scholar 

  7. Zhang, H., Ber, A., Florin, E., Swinney, H.: Collective motion and density fluctuations in bacterial colonies. Proc. Natl. Acad. Sci. 107, 13626–13630 (2010)

    Article  Google Scholar 

  8. Feynman, R.P.: Space-time approach to non-relativistic quantum mechanics. Rev. Mod. Phys. 20, 367–387 (1948)

    Article  MathSciNet  Google Scholar 

  9. Kleinert, H.: Path Integrals in Quantum Mechanics, Statistics, Polymer Physics, and Financial Markets, 3rd edn. World Scientific, Singapore (2004)

    Book  MATH  Google Scholar 

  10. Rudnick, J., Gaspari, G.: Elements of the Random Walk: An Introduction for Advanced Students and Researchers. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  11. Zhou, B., Tang, X., Zhang, H., et al.: Measuring crowd collectiveness. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1586–1599 (2014)

    Article  MathSciNet  Google Scholar 

  12. Zhang, W., Wang, X., Zhao, D., Tang, X.: Graph degree linkage: agglomerative clustering on a directed graph. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 428–441. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33718-5_31

    Chapter  Google Scholar 

  13. Biggs, N.: Algebraic Graph Theory. Cambridge University Press, Cambridge (1993)

    MATH  Google Scholar 

  14. Knuth, D.E.: The Art of Computer Programming, Volume 1 Fundamental Algorithms, 3rd edn. Addison-Wesley, Reading (1997)

    MATH  Google Scholar 

  15. Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., Shochet, O.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75, 1226–1229 (1995)

    Article  MathSciNet  Google Scholar 

  16. Buhl, J., Sumpter, D., Couzin, I., Hale, J., Despland, E., Miller, E., Simpson, S.: From disorder to order in marching locusts. Science 312, 1402–1406 (2006)

    Article  Google Scholar 

  17. Raafat, R.M., Chater, N., Frith, C.: Herding in humans. Trends Cogn. Sci. 13, 420–428 (2009)

    Article  Google Scholar 

  18. Zhao, D., Tang, X.: Cyclizing clusters via zeta function of a graph. In: Advances in Neural Information Processing Systems, pp. 1953–1960 (2009)

    Google Scholar 

  19. Liou, M.L.: A novel method of evaluating transient response. Proc. IEEE 54(1), 20–23 (1966)

    Article  MathSciNet  Google Scholar 

  20. Tomasi, C., Kanade, T.: Detection and tracking of point features. Int. J. Comput. Vis. (1991)

    Google Scholar 

  21. Chapelle, O., Zien, A.: Semi-supervised classification by low density separation. In: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pp. 57–64 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Science and Engineering, Officers College of Chinese Armed Police Force, Chengdu, 610213, Sichuan, China

    Wei-Ya Ren & Yun-Xiang Ling

  2. College of Information System and Management, National University of Defense Technology, Changsha, 410073, Hunan, China

    Guo-Hui Li & Yun-Xiang Ling

Authors
  1. Wei-Ya Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guo-Hui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun-Xiang Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei-Ya Ren .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Ren, WY., Li, GH., Ling, YX. (2016). Crowd Collectiveness Measure via Path Integral Descriptor. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_18

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3002-4_18

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3001-7

  • Online ISBN: 978-981-10-3002-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics