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Exploiting Video Classification Using Deep Learning Models for Human Activity Recognition

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Computer Vision and Robotics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

One of the most important and challenging research areas in the fields of Computer Vision is Human Activity Recognition (HAR). It has various applications including human-computer interaction, Intelligent driving. Intelligent video surveillance, human–robot interaction, ambient assisted living, etc. The major issues faced in this domain are feature extraction and feature selection which ends up in using handcrafted feature representation-based methods. This issue has been successfully addressed by Deep Learning techniques in various state-of-the-art classification methods for images and videos. But still which method is suited in which condition depends on the kind of dataset used for analysis. Deep Learning models like Convolutional Neural Networks (CNN), Variants of CNN, Recurrent Neural Networks (RNNs), and other fusion methods have shown promising results for a specific kind of activity related to HAR. Activity in HAR can be categorized as Gesture, Action, Interaction (Single-User, Multiple-User, Group). Each category has its own set of issues. This research aims to focus on Video Classification in bench marked datasets like UCF50, KTH, and Wizmann for Human gesture and Action Recognition which will then be confined to recognize the particular activity performed in the datasets. CNN and RNN fusion models such as Long Term Recurrent Convolutional Networks (LRCN) and ConvLSTM. These models performed efficiently of all the benchmarked datasets and hence can be utilized for Human Activity Recognition in arbitrary video dataset.

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Authors and Affiliations

  1. Defence Institute of Advanced Technology, Pune, India

    Upasna Singh & Nihit Singhal

Authors
  1. Upasna Singh
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  2. Nihit Singhal
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Correspondence to Upasna Singh .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Babu Banarasi Das University, Lucknow, Uttar Pradesh, India

    Praveen Kumar Shukla

  2. Department of Information Technology, Indian Institute of Information Technology, Allahabad, Prayagraj, Uttar Pradesh, India

    Krishna Pratap Singh

  3. Department of Computer Science & Engineering, Malviya National Institute of Technology (MNIT), Jaipur, India

    Ashish Kumar Tripathi

  4. Department of Industrial Engineering and Computer Science Division, University of Stellenbosch, Matieland, South Africa

    Andries Engelbrecht

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Singh, U., Singhal, N. (2023). Exploiting Video Classification Using Deep Learning Models for Human Activity Recognition. In: Shukla, P.K., Singh, K.P., Tripathi, A.K., Engelbrecht, A. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7892-0_14

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