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Skeleton-Based Human Activity Recognition Using Bidirectional LSTM

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 716))

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Abstract

Human activity recognition using 3D skeleton data has drawn the attention of research community. It is an interesting and well-sought-after computer vision problem whose prime objective is to determine actions performed by a human in a video or an image. The research work investigating human activity recognition in the 3D skeleton are still very limited. In this paper, we employ bidirectional long short-term memory (Bi-LSTM) deep learning model that utilizes skeleton information for modeling the dynamics in sequential data. The proposed model has been evaluated on Stony Brook University’s (SBU) dataset having two-person interactions with 8 different actions. Results demonstrate that the proposed method perform better than feature-based machine learning and state-of-the-art methods.

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Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Monika, Pardeep Singh & Satish Chand

Authors
  1. Monika
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  2. Pardeep Singh
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  3. Satish Chand
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Corresponding author

Correspondence to Monika .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Monika, Singh, P., Chand, S. (2023). Skeleton-Based Human Activity Recognition Using Bidirectional LSTM. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_15

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