Physical-barrier detection based collective motion analysis

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.Comparedwith the current collectivemotion 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

References

[1] Vicsek T, Zafeiris A. Collective motion. Physics Reports, 2012, 517: 71–140

[2] Ihaddadene N, Djeraba C. Real-time crowd motion analysis. In: Proceedings of International Conference on Pattern Recognition. 2008, 1–4

[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, 935–942

[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, 4657–4666

[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): 1–6

[6] Reynolds C W, Flocks. herds and schools: a distributed behavioral model. Computer Graphics, 1987, 21(4): 25–34

[7] Helbing D, Farkas I, Vicsek T. Simulating dynamical features of escape panic. Nature, 2000, 407(6803): 487–490

[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, 1928–1935

[9] Fiorini P, Shiller Z. Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 1998, 17(7): 760–772

[10] van den Berg J, Guy S J, Lin M, Manocha D. Reciprocal n-body collision avoidance. Robotics Research, 2011, 70: 3–19

[11] Zhou B, Tang X, Zhang H, Wang X. Measuring crowd collectiveness. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8): 1586–1599

[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): 583–598

[13] Shafarenko L, Petrou M, Kittler J. Automatic watershed segmentation of randomly textured color images. IEEE Transactions on Image Processing, 1997, 6(1): 1530–1544

[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): 783–789

[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): 1143–1157

[16] Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888–905

[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, 361–370

RIGHTS & PERMISSIONS

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