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International Conference on Computer Vision and Image Processing

CVIP 2021: Computer Vision and Image Processing pp 95–106Cite as

Dyadic Interaction Recognition Using Dynamic Representation and Convolutional Neural Network

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1567))

Abstract

Human interaction recognition can be used in video surveillance to recognise human behaviour. The goal of this research is to classify human interaction by converting video snippets into dynamic images and deep CNN architecture for classification. The human interaction input video is snipped into a certain number of smaller segments. For each segment, dynamic Image is constructed that efficiently encodes a video segment into an image with an action silhouette, which plays an important role in interaction recognition. The discriminative features are learned and classified from dynamic image using Convolutional Neural Network. The efficacy of the proposed architecture for interaction recognition is demonstrated by the obtained results on the SBU Kinect Interaction dataset, IXMAS, and TV Human Interaction datasets.

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Authors and Affiliations

  1. Centre for Image Processing and Pattern Recognition, Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, 626005, India

    R. Newlin Shebiah & S. Arivazhagan

Authors
  1. R. Newlin Shebiah
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  2. S. Arivazhagan
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Corresponding author

Correspondence to R. Newlin Shebiah .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Shebiah, R.N., Arivazhagan, S. (2022). Dyadic Interaction Recognition Using Dynamic Representation and Convolutional Neural Network. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_9

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  • DOI: https://doi.org/10.1007/978-3-031-11346-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11345-1

  • Online ISBN: 978-3-031-11346-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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