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Bag of Deep Features for Instructor Activity Recognition in Lecture Room

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MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11296))

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Abstract

This research aims to explore contextual visual information in the lecture room, to assist an instructor to articulate the effectiveness of the delivered lecture. The objective is to enable a self-evaluation mechanism for the instructor to improve lecture productivity by understanding their activities. Teacher’s effectiveness has a remarkable impact on uplifting students performance to make them succeed academically and professionally. Therefore, the process of lecture evaluation can significantly contribute to improve academic quality and governance. In this paper, we propose a vision-based framework to recognize the activities of the instructor for self-evaluation of the delivered lectures. The proposed approach uses motion templates of instructor activities and describes them through a Bag-of-Deep features (BoDF) representation. Deep spatio-temporal features extracted from motion templates are utilized to compile a visual vocabulary. The visual vocabulary for instructor activity recognition is quantized to optimize the learning model. A Support Vector Machine classifier is used to generate the model and predict the instructor activities. We evaluated the proposed scheme on a self-captured lecture room dataset, IAVID-1. Eight instructor activities: pointing towards the student, pointing towards board or screen, idle, interacting, sitting, walking, using a mobile phone and using a laptop, are recognized with an 85.41% accuracy. As a result, the proposed framework enables instructor activity recognition without human intervention.

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Acknowledgements

Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2014-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan

    Nudrat Nida & Muhammad Haroon Yousaf

  2. Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

    Aun Irtaza

  3. Department of Computer Science, Applied Artificial Intelligence Research Group, University Carlos III de Madrid, 28270, Madrid, Spain

    Sergio A. Velastin

  4. Cortexica Vision Systems Ltd., London, SE1 9LQ, UK

    Sergio A. Velastin

  5. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK

    Sergio A. Velastin

Authors
  1. Nudrat Nida
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  2. Muhammad Haroon Yousaf
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  3. Aun Irtaza
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  4. Sergio A. Velastin
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Corresponding author

Correspondence to Muhammad Haroon Yousaf .

Editor information

Editors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. EURECOM, Sophia Antipolis, France

    Benoit Huet

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Vasileios Mezaris

  4. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  5. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  6. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

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Nida, N., Yousaf, M.H., Irtaza, A., Velastin, S.A. (2019). Bag of Deep Features for Instructor Activity Recognition in Lecture Room. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_39

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  • DOI: https://doi.org/10.1007/978-3-030-05716-9_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05715-2

  • Online ISBN: 978-3-030-05716-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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