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Analysis of Various Video-Based Human Action Recognition Techniques Using Deep Learning Techniques

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

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Abstract

Human action recognition is the ability to identify and naming activities using Artificial Intelligence (AI) from the collected movement raw information through variety of resources. Distinguishing human activities from images or video sequences is a challenging task because of problems, including background untidiness, biased occlusion, and scale changes. In this survey, a complete reassess of modern and high-tech research advances in the field of human motion categorization is explicated. In particular, human activity recognition methods are classified into four categories according to the methods used. Moreover, the review is prepared based on the published year of the article, the method used for research, and performance metrics. Finally, the research gaps and concerns of systems are explained for raising an efficient practice for human action recognition techniques using deep learning approaches.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Tamil Nadu, India

    Lakshmi Alekhya Jandhyam & Ragupathy Rengaswamy

  2. Department of Computer Science and Engineering, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India

    Narayana Satyala

Authors
  1. Lakshmi Alekhya Jandhyam
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  2. Ragupathy Rengaswamy
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  3. Narayana Satyala
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Corresponding author

Correspondence to Lakshmi Alekhya Jandhyam .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jandhyam, L.A., Rengaswamy, R., Satyala, N. (2023). Analysis of Various Video-Based Human Action Recognition Techniques Using Deep Learning Techniques. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_16

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