LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Hao, Yu; Xu, Zhijie; Wang, Jing; Liu, Ying; Fan, Jiulun (2016)
Publisher: IEEE
Languages: English
Types: Part of book or chapter of book
Subjects: QA75
With the purpose of achieving automated detection of crowd abnormal behavior in public, this paper discusses the category of typical crowd and individual behaviors and their patterns. Popular image features for abnormal behavior detection are also introduced, including global flow based features such as optical flow, and local spatio-temporal based features such as Spatio-temporal Volume (STV). After reviewing some relative abnormal behavior detection algorithms, a brandnew approach to detect crowd panic behavior has been proposed based on optical flow features in this paper. During the experiments, all panic behaviors are successfully detected. In the end, the future work to improve current approach has been discussed.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] B. Solmaz, B. E. Moore, and M. Shah, “Identifying behaviors in crowd scenes using stability analysis for dynamical systems”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 10, pp. 2064-2070, Oct. 2012
    • [2] Teng Li, “Crowded scene analysis: a survey”. IEEE transactions on circuits and systems for video technology, vol. 25, no. 3, march 2015
    • [3] Ramin Mehran, B. E. Moore, and M. Shah, “A streakline representation of flow in crowded scenes”, in Proc. Eur. Conf. Comput. Vis., 2010, pp. 439-452.
    • [4] Hsin-Chun Tsai, “The optical flow-based analysis of human behaviorspecific system”. Orange Technologies (ICOT), 2013 International Conference on Year: 2013
    • [5] Ramin Mehran. “Abnormal crowd behavior detection using social force model”. 2009 IEEE
    • [6] E.H Adelson, J.R. Bergen, “Spatio-temporal energy models for the perception of motion”, Journal of the Optical Society of America A 2(1985) 284-299.
    • Wang J and Xu Z, “STV-based video feature processing for action recognition” [J] // Signal Processing, 2012, 93(8): 2151-2168.
    • [8] A. B. Chan and N. Vasconcelos. “Modeling, clustering, and segmenting video with mixtures of dynamic textures”. PAMI, 30(5):909-926, May 2008.
    • Vijay Mahadevan, “Anomaly detection in crowded scenes”, 2010 IEEE.
    • [10] Barbara Krausz and Christian Bauckhage. “Loveparade 2010: automatic video analysis of a crowd disaster”. In CVIU 116(2012) 307-319
    • [11] B. Solmaz, B. E. Moore, and M. Shah, “Identifying behaviors in crowd scenes using stability analysis for dynamical systems”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 10, pp. 2064-2070, Oct. 2012.
    • [12] A. Y. N. David M. Blei and M. I. Jordan. “Latent dirichlet allocation”. Journal of Machine Learning Research, 3:993- 1022, 2003.
    • [13] UMN Crowd Dataset. [Online]. Available: http://mha.cs.umn.edu/proj_ events.shtml#crowd
    • [14] Seung-Hwan Bae, and Kuk-Jin Yoon, “Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning”, 2014 IEEE Conference on Computer Vision and Pattern Recognition.Pages: 1218 - 1225
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article