Advanced search
1 file | 297.02 KB

PhD forum: multi-view occupancy maps using a network of low resolution visual sensors

Sebastian Grünwedel (UGent) , Vedran Jelača (UGent) , Peter Van Hese (UGent) , Richard Kleihorst (UGent) and Wilfried Philips (UGent)
Author
Organization
Abstract
An occupancy map provides an abstract top view of a scene and can be used for many applications such as domotics, surveillance, elderly-care and video teleconferencing. Such maps can be accurately estimated from multiple camera views. However, using a network of regular high resolution cameras makes the system expensive, and quickly raises privacy concerns (e. g. in elderly homes). Furthermore, their power consumption makes battery operation difficult. A solution could be the use of a network of low resolution visual sensors, but their limited resolution could degrade the accuracy of the maps. In this paper we used simulations to determine the minimum required resolution needed for deriving accurate occupancy maps which were then used to track people. Multi-view occupancy maps were computed from foreground silhouettes derived via an analysis of moving edges. Ground occupancies computed from each view were fused in a Dempster-Shafer framework. Tracking was done via a Bayes filter using the occupancy map per time instance as measurement. We found that for a room of 8.8 by 9.2 m, 4 cameras with a resolution as low as 64 by 48 pixels was sufficient to estimate accurate occupancy maps and track up to 4 people. These findings indicate that it is possible to use low resolution visual sensors to build a cheap, power efficient and privacy-friendly system for occupancy monitoring.
Keywords
occupancy monitoring, multi-camera tracking, foreground/background segmentation, occupancy map

Downloads

  • ICDSC SebastianGruenwedel.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 297.02 KB

Citation

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

Chicago
Grünwedel, Sebastian, Vedran Jelača, Peter Van Hese, Richard Kleihorst, and Wilfried Philips. 2011. “PhD Forum: Multi-view Occupancy Maps Using a Network of Low Resolution Visual Sensors.” In 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras. Piscataway, NJ, USA: IEEE.
APA
Grünwedel, S., Jelača, V., Van Hese, P., Kleihorst, R., & Philips, W. (2011). PhD forum: multi-view occupancy maps using a network of low resolution visual sensors. 2011 Fifth ACM/IEEE international conference on distributed smart cameras. Presented at the 5th ACM/IEEE International conference on Distributed Smart Cameras (ICDSC 2011), Piscataway, NJ, USA: IEEE.
Vancouver
1.
Grünwedel S, Jelača V, Van Hese P, Kleihorst R, Philips W. PhD forum: multi-view occupancy maps using a network of low resolution visual sensors. 2011 Fifth ACM/IEEE international conference on distributed smart cameras. Piscataway, NJ, USA: IEEE; 2011.
MLA
Grünwedel, Sebastian, Vedran Jelača, Peter Van Hese, et al. “PhD Forum: Multi-view Occupancy Maps Using a Network of Low Resolution Visual Sensors.” 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras. Piscataway, NJ, USA: IEEE, 2011. Print.
@inproceedings{1900822,
  abstract     = {An occupancy map provides an abstract top view of a scene and can be used for many applications such as domotics, surveillance, elderly-care and video teleconferencing. Such maps can be accurately estimated from multiple camera views. However, using a network of regular high resolution cameras makes the system expensive, and quickly raises privacy concerns (e. g. in elderly homes). Furthermore, their power consumption makes battery operation difficult. A solution could be the use of a network of low resolution visual sensors, but their limited resolution could degrade the accuracy of the maps. In this paper we used simulations to determine the minimum required resolution needed for deriving accurate occupancy maps which were then used to track people. Multi-view occupancy maps were computed from foreground silhouettes derived via an analysis of moving edges. Ground occupancies computed from each view were fused in a Dempster-Shafer framework. Tracking was done via a Bayes filter using the occupancy map per time instance as measurement. We found that for a room of 8.8 by 9.2 m, 4 cameras with a resolution as low as 64 by 48 pixels was sufficient to estimate accurate occupancy maps and track up to 4 people. These findings indicate that it is possible to use low resolution visual sensors to build a cheap, power efficient and privacy-friendly system for occupancy monitoring.},
  author       = {Gr{\"u}nwedel, Sebastian and Jela\v{c}a, Vedran and Van Hese, Peter and Kleihorst, Richard and Philips, Wilfried},
  booktitle    = {2011 Fifth ACM/IEEE international conference on distributed smart cameras},
  isbn         = {9781457717079},
  language     = {eng},
  location     = {Ghent, Belgium},
  pages        = {2},
  publisher    = {IEEE},
  title        = {PhD forum: multi-view occupancy maps using a network of low resolution visual sensors},
  year         = {2011},
}

Web of Science
Times cited: