Advertisement

Frontiers of Computer Science

, Volume 13, Issue 2, pp 426–436 | Cite as

Physical-barrier detection based collective motion analysis

  • Gaoqi He
  • Qi Chen
  • Dongxu Jiang
  • Yubo Yuan
  • Xingjian LuEmail author
Research Article
  • 9 Downloads

Abstract

Collective motion is one of the most fascinating phenomena and mainly caused by the interactions between individuals. Physical-barriers, as the particular facilities which divide the crowd into different lanes, greatly affect the measurement of such interactions. In this paper we propose the physical-barrier detection based collective motion analysis (PDCMA) approach. The main idea is that the interaction between spatially adjacent pedestrians actually does not exist if they are separated by the physical-barrier. Firstly, the physical-barriers are extracted by two-stage clustering. The scene is automatically divided into several motion regions. Secondly, local region collectiveness is calculated to represent the interactions between pedestrians in each region. Finally, extensive evaluations use the three typical methods, i.e., the PDCMA, the Collectiveness, and the average normalized Velocity, to show the efficiency and efficacy of our approach in the scenes with and without physical barriers. Moreover, several escalator scenes are selected as the typical physical-barrier test scenes to demonstrate the performance of our approach. Compared with the current collective motion analysis methods, our approach better adapts to the scenes with physical barriers.

Keywords

crowd behavior analysis collective motion physical-barrier detection two-stage clustering local region collectiveness 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was funded by the National Key Research and Development Program of China (2016YFA0502300), the National Natural Science Foundation of China (Grant No. 61602175), Shanghai Municipal Commission of Economy and Informatization (150809), the Open Research Funding Program of KLGIS (KLGIS2015A05) and BUAA (BUAAVR-15KF-03), the Fundamental Research Funds for the Central Universities (222201514331), and Green Manufacturing System Integration Project of Ministry of Industry and Technology of China (9908000006).

Supplementary material

11704_2018_7165_MOESM1_ESM.ppt (5 mb)
Physical-barrier detection based collective motion analysis

References

  1. 1.
    Vicsek T, Zafeiris A. Collective motion. Physics Reports, 2012, 517: 71–140CrossRefGoogle Scholar
  2. 2.
    Ihaddadene N, Djeraba C. Real-time crowd motion analysis. In: Proceedings of International Conference on Pattern Recognition. 2008, 1–4Google Scholar
  3. 3.
    Mehran R, Oyama A, Shah M. Abnormal crowd behavior detection using social force model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 935–942Google Scholar
  4. 4.
    Shao J, Kang K, Loy C C, Wang X. Deeply learned attributes for crowded scene understanding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 4657–4666Google Scholar
  5. 5.
    Bai Y, Xu Y, Yang X, Yan Q. Measuring orderliness based on social force model in collective motions. Visual Communications and Image Processing, 2013, 7(2): 1–6Google Scholar
  6. 6.
    Reynolds C W, Flocks. herds and schools: a distributed behavioral model. Computer Graphics, 1987, 21(4): 25–34CrossRefGoogle Scholar
  7. 7.
    Helbing D, Farkas I, Vicsek T. Simulating dynamical features of escape panic. Nature, 2000, 407(6803): 487–490CrossRefGoogle Scholar
  8. 8.
    Berg J V D, Lin M, Manocha D. Reciprocal velocity obstacles for realtime multi-agent navigation. In: Proceedings of IEEE International Conference on Robotics and Automation. 2008, 1928–1935Google Scholar
  9. 9.
    Fiorini P, Shiller Z. Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 1998, 17(7): 760–772CrossRefGoogle Scholar
  10. 10.
    van den Berg J, Guy S J, Lin M, Manocha D. Reciprocal n-body collision avoidance. Robotics Research, 2011, 70: 3–19CrossRefzbMATHGoogle Scholar
  11. 11.
    Zhou B, Tang X, Zhang H, Wang X. Measuring crowd collectiveness. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8): 1586–1599CrossRefGoogle Scholar
  12. 12.
    Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583–598CrossRefGoogle Scholar
  13. 13.
    Shafarenko L, Petrou M, Kittler J. Automatic watershed segmentation of randomly textured color images. IEEE Transactions on Image Processing, 1997, 6(1): 1530–1544CrossRefGoogle Scholar
  14. 14.
    Lezoray O, Cardot H. Cooperation of color pixel classification schemes and color watershed: a study for microscopic images. IEEE transactions on Image Processing, 2002, 11(7): 783–789CrossRefGoogle Scholar
  15. 15.
    Ye S, Liu C, Li Z. A double circle structure descriptor and Hough voting matching for real-time object detection. Pattern Analysis and Applications, 2016, 19(4): 1143–1157MathSciNetCrossRefGoogle Scholar
  16. 16.
    Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888–905CrossRefGoogle Scholar
  17. 17.
    Wu Y, Ye Y, Zhao C. Coherent motion detection with collective density clustering. In: Proceedings of the 23rd ACM International Conference on Multimedia. 2015, 361–370CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Gaoqi He
    • 1
    • 2
  • Qi Chen
    • 1
  • Dongxu Jiang
    • 1
  • Yubo Yuan
    • 1
  • Xingjian Lu
    • 1
    • 3
    Email author
  1. 1.Department of Computer Science and EngineeringEast China University of Science and TechnologyShanghaiChina
  2. 2.State Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijingChina
  3. 3.Smart City Collaborative Innovation CenterShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations