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Human Activity Recognition in Videos Using Deep Learning

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Soft Computing and Its Engineering Applications (icSoftComp 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1788))

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Abstract

Human Activity Recognition (HAR) is a challenging classification task. In the past, it traditionally involved the identification of the movement and activities of a person based on sensor inputs, apply signal processing to receive features and fit the features into a machine learning model. In recent times, deep learning methods have shown good results in automatic Human Activity Recognition. In this paper, we propose a pre-trained CNN (Inception-v3) and LSTM based methodology for Human Activity Recognition. The proposed methodology is evaluated on the publicly available UCF-101 dataset. The results show that the proposed methodology outperforms recent state-of-art methods in terms of accuracy (79.21%) and top-5 accuracy (92.92%) on the HAR task.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, NIT Hamirpur, Hamirpur, 177005, HP, India

    Mohit Kumar, Adarsh Rana,  Ankita, Arun Kumar Yadav & Divakar Yadav

Authors
  1. Mohit Kumar
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  2. Adarsh Rana
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  3. Ankita
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  4. Arun Kumar Yadav
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  5. Divakar Yadav
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Corresponding author

Correspondence to Mohit Kumar .

Editor information

Editors and Affiliations

  1. Charotar University of Science and Technology, Changa, Anand, Gujarat, India

    Kanubhai K. Patel

  2. University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  3. Charotar University of Science and Technology, Changa, Anand, India

    Atul Patel

  4. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

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Kumar, M., Rana, A., Ankita, Yadav, A.K., Yadav, D. (2023). Human Activity Recognition in Videos Using Deep Learning. In: Patel, K.K., Santosh, K.C., Patel, A., Ghosh, A. (eds) Soft Computing and Its Engineering Applications. icSoftComp 2022. Communications in Computer and Information Science, vol 1788. Springer, Cham. https://doi.org/10.1007/978-3-031-27609-5_23

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  • DOI: https://doi.org/10.1007/978-3-031-27609-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27608-8

  • Online ISBN: 978-3-031-27609-5

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