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Low-Intensity Human Activity Recognition Framework Using Audio Data in an Outdoor Environment

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

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Abstract

Audio-based activity recognition is an essential task in a wide range of human-centric applications. However, most of the work predominantly focuses on event detection, machine sound classification, road surveillance, scene classification, etc. There has been negligible attention to the recognition of low-intensity human activities for outdoor scenarios. This paper proposes a deep learning-based framework for recognizing different low-intensity human activities in a sparsely populated outdoor environment using audio. The proposed framework classifies 2.0 s long audio recordings into one of nine different activity classes. A variety of audio sounds in an outdoor environment makes it challenging to distinguish human activities from other background sounds. The proposed framework is an end-to-end architecture that employs a combination of mel-frequency cepstral coefficients and a 2D convolutional neural network to obtain a deep representation of activities and classify them. The extensive experimental analysis demonstrates that the proposed framework outperforms existing frameworks by 16.43% on the parameter F1-score. Additionally, we collected and provided an audio dataset for evaluation and benchmarking purposes to the research community.

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Authors and Affiliations

  1. Indian Institute of Technology Ropar, Rupnagar, India

    Priyankar Choudhary, Pratibha Kumari, Neeraj Goel & Mukesh Saini

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  1. Priyankar Choudhary
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  2. Pratibha Kumari
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  3. Neeraj Goel
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  4. Mukesh Saini
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Corresponding author

Correspondence to Priyankar Choudhary .

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Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Choudhary, P., Kumari, P., Goel, N., Saini, M. (2023). Low-Intensity Human Activity Recognition Framework Using Audio Data in an Outdoor Environment. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_49

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  • DOI: https://doi.org/10.1007/978-3-031-31417-9_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

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