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Learning about objects in the meeting rooms from people trajectories

Xingzhe Xie (UGent) , Sebastian Grünwedel (UGent) , Vedran Jelača (UGent) , Jorge Niño Castañeda (UGent) , Dirk Van Haerenborgh (UGent) , Dimitri Van Cauwelaert (UGent) , Peter Van Hese (UGent) , Peter Veelaert (UGent) , Wilfried Philips (UGent) and Hamid Aghajan (UGent)
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Abstract
In ambient intelligence object recognition is an important step towards behaviour analysis and the understanding interactions between people and the environment. Existing methods focus on a detailed analysis of image content using colour, shape, texture and motion analysis (direct recognition). In this paper we present a method for recognizing furniture, i.e. chairs, tables and the walking area in a meeting room using the estimated trajectories of people (indirect recognition). We use Support Vector Machines (SVMs) to classify the activities into three categories: sitting, standing and walking to create two occupancy maps for sitting and walking spaces according to Bayesian theory. The positions of the chairs and tables are inferred from these maps. We compared the recognition of chairs and tables to ground truth data on meeting scenarios. The performance of this method is good.
Keywords
SVM, smart distributed cameras, object recognition, occupancy map, activity analysis, RECOGNITION, SVM

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Citation

Please use this url to cite or link to this publication:

Chicago
Xie, Xingzhe, Sebastian Grünwedel, Vedran Jelača, Jorge Niño Castañeda, Dirk Van Haerenborgh, Dimitri Van Cauwelaert, Peter Van Hese, Peter Veelaert, Wilfried Philips, and Hamid Aghajan. 2012. “Learning About Objects in the Meeting Rooms from People Trajectories.” In 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC). New York, NY, USA: IEEE.
APA
Xie, X., Grünwedel, S., Jelača, V., Niño Castañeda, J., Van Haerenborgh, D., Van Cauwelaert, D., Van Hese, P., et al. (2012). Learning about objects in the meeting rooms from people trajectories. 2012 Sixth international conference on distributed smart cameras (ICDSC). Presented at the 6th ACM/IEEE International conference on Distributed Smart Cameras (ICDSC 2012), New York, NY, USA: IEEE.
Vancouver
1.
Xie X, Grünwedel S, Jelača V, Niño Castañeda J, Van Haerenborgh D, Van Cauwelaert D, et al. Learning about objects in the meeting rooms from people trajectories. 2012 Sixth international conference on distributed smart cameras (ICDSC). New York, NY, USA: IEEE; 2012.
MLA
Xie, Xingzhe, Sebastian Grünwedel, Vedran Jelača, et al. “Learning About Objects in the Meeting Rooms from People Trajectories.” 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC). New York, NY, USA: IEEE, 2012. Print.
@inproceedings{3104722,
  abstract     = {In ambient intelligence object recognition is an important step towards behaviour analysis and the understanding interactions between people and the environment. Existing methods focus on a detailed analysis of image content using colour, shape, texture and motion analysis (direct recognition). In this paper we present a method for recognizing furniture,
i.e. chairs, tables and the walking area in a meeting room using the estimated trajectories of people (indirect recognition). We use Support Vector Machines (SVMs) to classify the activities into three categories: sitting, standing and walking to create two occupancy maps for sitting and walking spaces according to Bayesian theory. The positions of the chairs and tables are inferred from these maps. We compared the recognition of chairs and tables to ground truth data on meeting scenarios. The performance of this method is good.},
  author       = {Xie, Xingzhe and Gr{\"u}nwedel, Sebastian and Jela\v{c}a, Vedran and Ni{\~n}o Casta{\~n}eda, Jorge and Van Haerenborgh, Dirk and Van Cauwelaert, Dimitri and Van Hese, Peter and Veelaert, Peter and Philips, Wilfried and Aghajan, Hamid},
  booktitle    = {2012 Sixth international conference on distributed smart cameras (ICDSC)},
  isbn         = {9781450317726},
  keyword      = {SVM,smart distributed cameras,object recognition,occupancy map,activity analysis,RECOGNITION,SVM},
  language     = {eng},
  location     = {Hong Kong, PR China},
  pages        = {6},
  publisher    = {IEEE},
  title        = {Learning about objects in the meeting rooms from people trajectories},
  year         = {2012},
}

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