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An Improved Approach to Crowd Event Detection by Reducing Data Dimensions

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Advances in Signal Processing and Intelligent Recognition Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 425))

Abstract

Crowd monitoring is a critical application in video surveillance. Crowd events such as running, walking, merging, splitting, dispersion, and evacuation inform crowd management about the behavior of groups of people. For an effective crowd management, detection of crowd events provides an early sign of the behavior of the people. However, crowd event detection using videos is a highly challenging task because of several challenges such as non-rigid human body motions, occlusions, unavailability of distinguishing features due to occlusions, unpredictability in people movements, and other. In addition, the video itself is a high-dimensional data and analyzing to detect events becomes further complicated. One way of tackling the huge volume of video data is to represent a video using low-dimensional equivalent. However, reducing the video data size needs to consider the complex data structure and events embedded in a video. To this extent, we focus on detection of crowd events using the Isometric Mapping (ISOMAP) and Support Vector Machine (SVM). The ISOMAP is used to construct the low-dimensional representation of the feature vectors, and then an SVM is used for training and classification. The proposed approach uses Haar wavelets to extract Gray Level Coefficient Matrix (GLCM). Later, the approach extracts four statistical features (contrast, correlating, energy, and homogeneity) at different levels of Haar wavelet decomposition. Experiment results suggest that the proposed approach is shown to perform better when compared with existing approaches.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville Campus, Melbourne, VIC, 3010, Australia

    Aravinda S. Rao

  2. TCS Innovation Labs, Tata Consultancy Services, Bengaluru, Karnataka, 560066, India

    Jayavardhana Gubbi

  3. Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville Campus, Melbourne, VIC, 3010, Australia

    Marimuthu Palaniswami

Authors
  1. Aravinda S. Rao
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  2. Jayavardhana Gubbi
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  3. Marimuthu Palaniswami
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Corresponding author

Correspondence to Aravinda S. Rao .

Editor information

Editors and Affiliations

  1. and Management – Kerala (IIITM-K), Indian Inst. of Information Technology, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. Department of Electrical, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. Computer Engineering Department, Vladimir State University, Vladimir Region, Russia

    Sergey Mosin

  6. School of Electrical, Nanyang Technological University, Singapore, Singapore

    Maode Ma

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Rao, A.S., Gubbi, J., Palaniswami, M. (2016). An Improved Approach to Crowd Event Detection by Reducing Data Dimensions. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-28658-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28656-3

  • Online ISBN: 978-3-319-28658-7

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