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SV-NET: A Deep Learning Approach to Video Based Human Activity Recognition

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Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) (SoCPaR 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1182))

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Abstract

The automatic identification of physical activities performed by human beings is referred to as Human Activity Recognition (HAR). It aims to infer the actions of one or more persons from a set of observations captured by sensors, videos or still images. Recognizing human activities from video sequences is a much challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance etc. In this paper, we propose a Convolutional Neural Network (CNN) model named as SV-NET, in order to classify human activities obtained directly from RGB videos. The proposed model has been tested on three benchmark video datasets namely, KTH, UCF11 and HMDB51. The results of the proposed model demonstrate improved performance over some existing deep learning based models.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India

    Sukrit Bhattacharya

  2. Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Kolkata, India

    Vaibhav Shaw

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Pawan Kumar Singh, Ram Sarkar & Debotosh Bhattacharjee

Authors
  1. Sukrit Bhattacharya
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  2. Vaibhav Shaw
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  3. Pawan Kumar Singh
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  4. Ram Sarkar
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  5. Debotosh Bhattacharjee
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Corresponding author

Correspondence to Pawan Kumar Singh .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India

    M. A. Jabbar

  3. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain

    Sanju Tiwari

  4. ISEP - Instituto Superior de Engenharia do Porto, Porto, Portugal

    Isabel M. S. Jesus

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The authors declare that there is no conflict of interests regarding the publication of this paper.

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Bhattacharya, S., Shaw, V., Singh, P.K., Sarkar, R., Bhattacharjee, D. (2021). SV-NET: A Deep Learning Approach to Video Based Human Activity Recognition. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_2

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