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Discovering activity patterns in office environment using a network of low-resolution visual sensors

Mohamed Eldib (UGent) , Francis Deboeverie (UGent) , Wilfried Philips (UGent) and Hamid Aghajan (UGent)
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Abstract
Understanding activity patterns in office environments is important in order to increase workers’ comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users’ locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users’ mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual’s tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user’s presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user’s status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons’ daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group’s activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events.
Keywords
Visual sensor network, Supervised learning, Probabilistic graphical models, Topic models, Sequence mining

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Chicago
Eldib, Mohamed, Francis Deboeverie, Wilfried Philips, and Hamid Aghajan. 2018. “Discovering Activity Patterns in Office Environment Using a Network of Low-resolution Visual Sensors.” Journal of Ambient Intelligence and Humanized Computing 9 (2): 381–411.
APA
Eldib, M., Deboeverie, F., Philips, W., & Aghajan, H. (2018). Discovering activity patterns in office environment using a network of low-resolution visual sensors. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING , 9(2), 381–411.
Vancouver
1.
Eldib M, Deboeverie F, Philips W, Aghajan H. Discovering activity patterns in office environment using a network of low-resolution visual sensors. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING . Springer Nature; 2018;9(2):381–411.
MLA
Eldib, Mohamed, Francis Deboeverie, Wilfried Philips, et al. “Discovering Activity Patterns in Office Environment Using a Network of Low-resolution Visual Sensors.” JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 9.2 (2018): 381–411. Print.
@article{8526738,
  abstract     = {Understanding activity patterns in office environments is important in order to increase workers{\textquoteright} comfort and productivity. This paper proposes an automated system for discovering activity patterns of multiple persons in a work environment using a network of cheap low-resolution visual sensors (900 pixels). Firstly, the users{\textquoteright} locations are obtained from a robust people tracker based on recursive maximum likelihood principles. Secondly, based on the users{\textquoteright} mobility tracks, the high density positions are found using a bivariate kernel density estimation. Then, the hotspots are detected using a confidence region estimation. Thirdly, we analyze the individual{\textquoteright}s tracks to find the starting and ending hotspots. The starting and ending hotspots form an observation sequence, where the user{\textquoteright}s presence and absence are detected using three powerful Probabilistic Graphical Models (PGMs). We describe two approaches to identify the user{\textquoteright}s status: a single model approach and a two-model mining approach. We evaluate both approaches on video sequences captured in a real work environment, where the persons{\textquoteright} daily routines are recorded over 5 months. We show how the second approach achieves a better performance than the first approach. Routines dominating the entire group{\textquoteright}s activities are identified with a methodology based on the Latent Dirichlet Allocation topic model. We also detect routines which are characteristic of persons. More specifically, we perform various analysis to determine regions with high variations, which may correspond to specific events.},
  author       = {Eldib, Mohamed and Deboeverie, Francis and Philips, Wilfried and Aghajan, Hamid},
  issn         = {1868-5145},
  journal      = {JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING },
  keyword      = {Visual sensor network,Supervised learning,Probabilistic graphical models,Topic models,Sequence mining},
  language     = {eng},
  number       = {2},
  pages        = {381--411},
  publisher    = {Springer Nature},
  title        = {Discovering activity patterns in office environment using a network of low-resolution visual sensors},
  url          = {http://dx.doi.org/10.1007/s12652-017-0511-7},
  volume       = {9},
  year         = {2018},
}

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