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Unusual Crowd Event Detection: An Approach Using Probabilistic Neural Network

  • B. H. LohithashvaEmail author
  • V. N. Manjunath Aradhya
  • H. T. Basavaraju
  • B. S. Harish
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)

Abstract

Unusual event detection is a disputing problem in the field of security assets for public places. In this paper, we proposed detection of unusual crowded events in video established on interest point information. The distribution of magnitude and orientation holistic feature descriptor extracted from the histogram of optical flow for detection of usual and unusual events. In the proposed method, the probabilistic neural network approach is employed for unusual event detection in video. The reason for using PNN is more accurate, fast in training process and much faster than support vector machine. The proposed method used three benchmark datasets, UMN, PETS2009, and violent flows datasets, for the experiment. The obtained results compared with state-of-the-art methods, and improved outcomes are depicted to evaluate the outperformance of the proposed method.

Keywords

Unusual crowd Feature descriptor Videos HOFO DiMOGIF PNN 

Notes

Acknowledgements

B. H. Lohithashva has been financially supported by UGC under Rajiv Gandhi National Fellowship (RGNF) Letter no. F1-17.1/2014-15/RGNF-2014-15-SC-KAR-73791/(SA-III/Website), Sri Jayachamarajendra College of Engineering, Mysuru, VTU, Karnataka, India.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • B. H. Lohithashva
    • 1
    Email author
  • V. N. Manjunath Aradhya
    • 1
  • H. T. Basavaraju
    • 1
  • B. S. Harish
    • 2
  1. 1.Department of Master of Computer ApplicationsJSS Science and Technology University (Sri Jayachamarajendra College of Engineering)MysuruIndia
  2. 2.Department of Information Science and EngineeringJSS Science and Technology University (Sri Jayachamarajendra College of Engineering)MysuruIndia

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