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An Efficient Approach to Feature Extraction for Crowd Density Estimation

  • Neeta Anil Nemade
  • V. V. Gohokar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

Crowd feature extraction is important step for crowd density estimation. This paper proposes a simple and novel approach of feature extraction applicable for crowd density estimation. A 5 × 5 mask is proposed for extraction of density, which finds isolated components in the image. This helps in classification of the density in five levels using SVM and ANN classifiers. The method can be used for intelligent surveillance system in public places. It can easily be used for embedded applications.

Keywords

Support vector machine (SVM) Artificial neural network (ANN) Convolution of mask Isolated components Statistical features Thresholding 

References

  1. 1.
    Loy, C.C., Chen, K., Gong, S., Xiang, T.: Crowd Counting and Profiling: Methodology and Evaluation (2013)CrossRefGoogle Scholar
  2. 2.
    Saleh, S.A.M., Suandi, S.A., Ibrahim, H.: Recent survey on crowd density estimation and counting for visual surveillance, pp. 103–114. Elsevier Ltd. (2015)Google Scholar
  3. 3.
    Polus, A., Schofer, J., Ushpiz, A.: Pedestrian flow and level of service. J. Transp. Eng. 109(1), 46–56 (1983)CrossRefGoogle Scholar
  4. 4.
    Nemade, N.A., Gohokar, V.V.: Feature representation for crowd counting by regression: a review. Int. J. Adv. Res. Comput. Commun. Eng. 5(1), 402–405 (2016)Google Scholar
  5. 5.
    Nemade, N.A., Gohokar, V.V.: A survey of video datasets for crowd density estimation. In: International Conference on Global Trends in Signal Processing, Information Computing and Communication, pp. 389–395. IEEE (2016)Google Scholar

Book Chapter

  1. 6.
    Nemade, N.A., Gohokar, V.V.: Crowd Density as Dynamic Texture: Behavior Estimation and Classification, Information and Communication Technology, AISC, vol. 625, www.springer.com/us/book/9789811055072 (2016)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Electronics & Telecommunication Engineering DepartmentSSGM College of EngineeringShegaonIndia
  2. 2.Electronics & Telecommunication Engineering DepartmentMaharashtra Institute of TechnologyKothrud, PuneIndia

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