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Action Recognition Using Local Visual Descriptors and Inertial Data

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Book cover Ambient Intelligence (AmI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11912))

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Abstract

Different body sensors and modalities can be used in human action recognition, either separately or simultaneously. Multi-modal data can be used in recognizing human action. In this work we are using inertial measurement units (IMUs) positioned at left and right hands with first person vision for human action recognition. A novel statistical feature extraction method was proposed based on curvature of the graph of a function and tracking left and right hand positions in space. Local visual descriptors have been used as features for egocentric vision. An intermediate fusion between IMUs and visual sensors has been performed. Despite of using only two IMUs sensors with egocentric vision, our classification result achieved is 99.61% for recognizing nine different actions. Feature extraction step could play a vital step in human action recognition with limited number of sensors, hence, our method might indeed be promising.

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Notes

  1. 1.

    https://github.com/alhersh/ActionExtractor.

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Author information

Authors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Taha Alhersh & Heiner Stuckenschmidt

  2. College of Science and Engineering, Hamad Bin Khalifa University, Education City, Doha, Qatar

    Samir Brahim Belhaouari

Authors
  1. Taha Alhersh
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  2. Samir Brahim Belhaouari
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  3. Heiner Stuckenschmidt
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Correspondence to Taha Alhersh .

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Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Ioannis Chatzigiannakis

  2. Philips Research, Eindhoven, The Netherlands

    Boris De Ruyter

  3. Hellenic Open University, Patras, Greece

    Irene Mavrommati

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Alhersh, T., Brahim Belhaouari, S., Stuckenschmidt, H. (2019). Action Recognition Using Local Visual Descriptors and Inertial Data. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-34255-5_9

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  • Publisher Name: Springer, Cham

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