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Activity Detection and Recognition of Daily Living Events

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Health Monitoring and Personalized Feedback using Multimedia Data

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Abstract

Detection and recognition of Activities of Daily Living (ADLs) from visual data is a useful tool for unobtrusive home environment monitoring. ADLs are detected spatio-temporally in long videos, while activity recognition is applied for the purposes of human behaviour analysis and life logging. We propose a novel ADL detection schema for the fast and accurate temporal localization of activities of daily living. At the same time, a novel representation, based on trajectories extracted in Activity Areas, along with a hybrid feature descriptor, are proposed to improve ADL recognition results. A temporal sliding window approach is used to classify overlapping time intervals using a multi-class SVM classifier, while a voting procedure accumulates recognition results in order to detect ambiguous time intervals. The proposed framework is tested on realistic scenarios of daily living, in videos recorded in home and lab environments, while accuracy rates on benchmark datasets are also provided.

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Acknowledgements

This work was funded by the European Commission under the 7th Framework Program (FP7 2007–2013), grant agreement 288199 Dem@Care.

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Authors and Affiliations

  1. Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Konstantinos Avgerinakis, Alexia Briassouli & Ioannis Kompatsiaris

Authors
  1. Konstantinos Avgerinakis
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  2. Alexia Briassouli
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  3. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Konstantinos Avgerinakis .

Editor information

Editors and Affiliations

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

    Alexia Briassouli

  2. Laboratoire Bordelais de Recherche en Informatique/University Bordeaux, Talence, France

    Jenny Benois-Pineau

  3. Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Alexander Hauptmann

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Avgerinakis, K., Briassouli, A., Kompatsiaris, I. (2015). Activity Detection and Recognition of Daily Living Events. In: Briassouli, A., Benois-Pineau, J., Hauptmann, A. (eds) Health Monitoring and Personalized Feedback using Multimedia Data. Springer, Cham. https://doi.org/10.1007/978-3-319-17963-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-17963-6_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17962-9

  • Online ISBN: 978-3-319-17963-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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