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Crowd Density as Dynamic Texture: Behavior Estimation and Classification

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Information and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 625))

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Abstract

Extracting crowd feature is a key step for crowd density estimation. This paper proposes a simple and novel approach of preprocessing and extraction of crowd feature. A 5 × 5 mask is defined for finding isolated components in the image, which proved very efficient for classification of crowd density. SVM classifier is used for classifying the crowd in five different levels. The proposed method is powerful to understand crowd behavior such as crowd coming toward camera and exiting from the camera site. The results are analyzed for PETS dataset and are very promising for images that have bright sunlight and shadow frames too. This method can be used for intelligent surveillance system in public places.

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References

  1. A.N. Marana, S. A. Velastin, L. F. Costa, ‘Estimation of crowd density using image processing’, Image Processing for Security Applications, IEE Colloquium, 1997.

    Google Scholar 

  2. A.N. Marana, S.A. Velastin, L.F. Costa, R. A. Lotufo. “Automatic estimation of crowd density using texture”, International Workshop on Systems and Image Processing, IWSIP’97, May 28–30, Poland.

    Google Scholar 

  3. A.N. Marana, L. da Costa, R. Lotufo, and S. Velastin. On the efficacy of texture analysis for crowd monitoring’, Proceedings of the International Symposium on Computer Graphics, pages 354–361, 1998.

    Google Scholar 

  4. A.N. Marana, S. Velastin, L.F. Costa and R. Lotufo, Automatic Estimation of Crowd Density Using Texture. Safety Science, Vol. 28, pp: 165–175, 1998.

    Google Scholar 

  5. Marana A. N. Verona V, “Wavelet packet analysis for crowd density estimation,” International Symposia on Applied Informatics, Inns-bruck, Austria, pp. 535–540, 2001.

    Google Scholar 

  6. A.N. Marana and M.A. Cavenaghi, Real-Time Crowd Density Estimation Using Images. International Symposium on Visual Computing, pp: 355–362, 2005.

    Google Scholar 

  7. Haibin Yu, Zhiwei He, Yuanyuan Liu, Li Zhang, “A Crowd Flow Estimation Method Based On Dynamic Texture and GRNN”, IEEE, 2011.

    Google Scholar 

  8. Wei Li and Xiaojuan Wu, Koichi Matsumoto and Hua -An Zhao, “A New Approach of Crowd Density Estimation”, TENCON 2010, IEEE.

    Google Scholar 

  9. Hajer Fradi, Xuran Zhao and Jean-Luc Dugelay, “Crowd Density Analysis Using Subspace Learning On Local Binary Pattern”, July 2013.

    Google Scholar 

  10. Hajer Fradi, Jean-Luc Dugelay, A New Multiclass SVM Algorithm and its Application to Crowd Density Analysis Using LBP Features, ICIP 2013, IEEE.

    Google Scholar 

  11. Hua Yang, Hang Su, Shibao Zheng, Sha Wei and Yawen Fan, “The Large-Scale Crowd Density Estimation Based on Sparse Spatiotemporal Local Binary Pattern”, IEEE, 2011.

    Google Scholar 

  12. Zhe Wang, Hong Liu, Yueliang Qian and Tao Xu, “Crowd Density Estimation Based on Local Binary Pattern Co-Occurrence Matrix,” 2012 IEEE International Conference on Multimedia and Expo Workshop.

    Google Scholar 

  13. Gidudu Anthony, Hulley Gregg and Marwala Tshilidzi, ‘Image Classification Using SVMs: One-against-One Vs One-against-All’, 2007.

    Google Scholar 

  14. Jonathan Milgram, Mohamed Cheriet, Robert Sabourin, ‘“One Against One” or “One Against All”: Which One is Better for Handwriting Recognition with SVMs?, Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 5 2006.

    Google Scholar 

  15. A. Polus, J. Schofer and A. Ushpiz, “Pedestrian Flow and Level of Service,” J. Transportation Eng. Volume 19, Issue 5–6, September 2008. Pages 345–357.

    Google Scholar 

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Acknowledgements

The authors would like to thank to the PETS dataset for providing the crowd counting datasets.

Author information

Authors and Affiliations

  1. Shri Sant Gadge Baba College of Engineering & Technology, Bhusawal, India

    Neeta A. Nemade

  2. Maharashtra Institute of Technology, Kothrud, Pune, India

    V. V. Gohokar

Authors
  1. Neeta A. Nemade
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  2. V. V. Gohokar
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Corresponding author

Correspondence to Neeta A. Nemade .

Editor information

Editors and Affiliations

  1. Microsoft Innovation Centre, Sri Aurobindo Institute of Technology Microsoft Innovation Centre, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  2. Faculty of Computers and Information, Banha University Faculty of Computers and Information, Banha, Egypt

    Ahmad Taher Azar

  3. Sabar Institute of Technology, Asst. Prof., Dept. of Info. Tech. Sabar Institute of Technology, Gujarat, Gujarat, India

    Amit Joshi

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© 2018 Springer Nature Singapore Pte Ltd.

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Nemade, N.A., Gohokar, V.V. (2018). Crowd Density as Dynamic Texture: Behavior Estimation and Classification. In: Mishra, D., Azar, A., Joshi, A. (eds) Information and Communication Technology . Advances in Intelligent Systems and Computing, vol 625. Springer, Singapore. https://doi.org/10.1007/978-981-10-5508-9_9

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  • DOI: https://doi.org/10.1007/978-981-10-5508-9_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5507-2

  • Online ISBN: 978-981-10-5508-9

  • eBook Packages: EngineeringEngineering (R0)

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