Abstract
There is a great demand for automated systems which can easily investigate the behavior of crowd using video surveillance. Crowd exploration is critical for human comportment investigation, well-being science, and computational recreation and computer vision applications. Numerous techniques have been proposed in this area by many researchers. In aggregation of the activities, the analysis of human behavior in crowd has numerous substantial qualities like speed, heading of movement, cooperation power, and vitality. In this way, a ton of techniques and models inspired from such physical thoughts are connected in numerous systems for identifying crowd comportment investigation. This paper reviews some of methods based on physical techniques for crowd investigation in detail. On the basis of such physics, here crowd analysis is outlined into three major classes, viz., interaction force, complex situations of crowd movement frameworks and fluid dynamics. Some crowd-based databases are analyzed in this paper along with future work directions.
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References
Shiwakoti, N., Gong, Y., Shi, X., Ye, Z.: Examining influence of merging architectural features on pedestrian crowd movement. Saf. Sci. 75, 15–22 (2015)
Karamouzas, I., Overmars, M.: Simulating and evaluating the local behavior of small pedestrian groups. IEEE Trans. Vis. Comput. Graph. 18(3), 394–406 (2012)
Xu, M., Wu, Y., Lv, P., Jiang, H., Luo, M., Ye, Y.: miSFM: on combination of mutual information and social force model towards simulating crowd evacuation. Neurocomputing 168, 529–537 (2015)
Wu, S., San Wong, H.: Crowd motion partitioning in a scattered motion field. IEEE Trans. Syst. Man Cybern. Part B 42(5), 1443–1454 (2012)
Cong, Y., Yuan, J., Liu, J.: Abnormal event detection in crowded scenes using sparse representation. Pattern Recognit. 46(7), 1851–1864 (2013)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 935–942 (2009)
Cheng, G., Wan, Y., Saudagar, A.N., Namuduri, K., Buckles, B.P.: Advances in human action recognition: a survey (2015). arXiv preprint arXiv:1501.05964
Ge, W., Collins, R.T.: Marked point processes for crowd counting. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, CVPR 2009, pp. 2913–2920 (2009)
Guo, P., Miao, Z.: Action detection in crowded videos using masks. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1767–1770 (2010)
Basavaraj, G.M., Kusagur, A.: Optical and streakline flow based crowd estimation for surveillance system, In: IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 414–416 (2016)
Zhang, D., Xu, J., Sun, M., Xiang, Z.: High-density crowd behaviors segmentation based on dynamical systems. Multimed. Syst. 23(5), 599–606 (2017)
Wu, S., Su, H., Yang, H., Zheng, S., Fan, Y., Zhou, Q.: Bilinear dynamics for crowd video analysis. J. Vis. Commun. Image Represent. 48, 461–470 (2017)
Lim, M.K., Chan, C.S., Monekosso, D., Remagnino, P.: Detection of salient regions in crowded scenes. Electron. Lett. 50(5), 363–365 (2014)
Hu, M., Ali, S., Shah, M.: Detecting global motion patterns in complex videos. In: 19th International Conference on Pattern Recognition, 2008, ICPR, 2008, pp. 1–5 (2008)
Khan, S.D., Vizzari, G., Bandini, S.: Identifying sources and sinks and detecting dominant motion patterns in crowds. Transp. Res. Procedia 2, 195–200 (2014)
Khan, S.D., Bandini, S., Basalamah, S., Vizzari, G.: Analyzing crowd behavior in naturalistic conditions: Identifying sources and sinks and characterizing main flows. Neurocomputing 177, 543–563 (2016)
Yamaguchi, K., Berg, A.C., Ortiz, L.E., Berg, T.L.: Who are you with and where are you going? In: Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1345–1352 (2011)
Bellomo, N., Piccoli, B., Tosin, A.: Modeling crowd dynamics from a complex system viewpoint. Math. Model. methods Appl. Sci. 22(supp02), 1230004 (2012)
Chen, D.-Y., Huang, P.-C.: Visual-based human crowds behavior analysis based on graph modeling and matching. IEEE Sens. J. 13(6), 2129–2138 (2013)
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Bhati, B.K., Aggawal, A. (2020). A Survey on Crowd Video Exploration Using Physical Enthused Approaches. In: Somani, A.K., Shekhawat, R.S., Mundra, A., Srivastava, S., Verma, V.K. (eds) Smart Systems and IoT: Innovations in Computing. Smart Innovation, Systems and Technologies, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-13-8406-6_74
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DOI: https://doi.org/10.1007/978-981-13-8406-6_74
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