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Anomaly Detection in Crowd Using Optical Flow and Textural Feature

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Soft Computing and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 900))

Abstract

This paper aims at solving real-world surveillance problems using computer vision and motion estimation techniques. It focuses on detecting abnormal crowd behaviour and locating it in dynamic crowd condition. In this paper, a combined approach is the proposed using the crowd motion analysis and texture-based analysis. Lucas–Kanade optical flow method is used for the estimation of motion in the scene. Also, texture-based feature and entropy give the statistical measure of randomness which is used for localization of crowd. The University of Minnesota (UMN) database has been used for testing.

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Author information

Authors and Affiliations

  1. (COEP) College of Engineering, Pune, India

    Pranali Ingole & Vibha Vyas

Authors
  1. Pranali Ingole
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  2. Vibha Vyas
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Corresponding author

Correspondence to Pranali Ingole .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  2. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangaluru, Karnataka, India

    G. Ram Mohana Reddy

  3. Department of Computer Science and Engineering, JNTUH College of Engineering Hyderabad, Hyderabad, Telangana, India

    V. Kamakshi Prasad

  4. Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, Telangana, India

    V. Sivakumar Reddy

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© 2019 Springer Nature Singapore Pte Ltd.

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Ingole, P., Vyas, V. (2019). Anomaly Detection in Crowd Using Optical Flow and Textural Feature. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-13-3600-3_69

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