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A low resolution multi-camera system for person tracking

Mohamed Eldib (UGent) , Bo Bo Nyan (UGent) , Francis Deboeverie (UGent) , Jorge Niño Castañeda (UGent) , Junzhi Guan (UGent) , Samuel Van de Velde, Heidi Steendam (UGent) , Hamid Aghajan (UGent) and Wilfried Philips (UGent)
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Organization
Abstract
The current multi-camera systems have not studied the problem of person tracking under low resolution constraints. In this paper, we propose a low resolution sensor network for person tracking. The network is composed of cameras with a resolution of 30x30 pixels. The multi-camera system is used to evaluate probability occupancy mapping and maximum likelihood trackers against ground truth collected by ultra-wideband (UWB) testbed. Performance evaluation is performed on two video sequences of 30 minutes. The experimental results show that maximum likelihood estimation based tracker outperforms the state-of-the-art on low resolution cameras
Keywords
Low resolution multi-camera systems, foreground detection, tracking, behavior analysis

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

MLA
Eldib, Mohamed, Bo Bo Nyan, Francis Deboeverie, et al. “A Low Resolution Multi-camera System for Person Tracking.” IEEE International Conference on Image Processing ICIP. Piscataway, NJ, USA: IEEE, 2014. 378–382. Print.
APA
Eldib, M., Nyan, B. B., Deboeverie, F., Niño Castañeda, J., Guan, J., Van de Velde, S., Steendam, H., et al. (2014). A low resolution multi-camera system for person tracking. IEEE International Conference on Image Processing ICIP (pp. 378–382). Presented at the IEEE International Conference on Image Processing (ICIP), Piscataway, NJ, USA: IEEE.
Chicago author-date
Eldib, Mohamed, Bo Bo Nyan, Francis Deboeverie, Jorge Niño Castañeda, Junzhi Guan, Samuel Van de Velde, Heidi Steendam, Hamid Aghajan, and Wilfried Philips. 2014. “A Low Resolution Multi-camera System for Person Tracking.” In IEEE International Conference on Image Processing ICIP, 378–382. Piscataway, NJ, USA: IEEE.
Chicago author-date (all authors)
Eldib, Mohamed, Bo Bo Nyan, Francis Deboeverie, Jorge Niño Castañeda, Junzhi Guan, Samuel Van de Velde, Heidi Steendam, Hamid Aghajan, and Wilfried Philips. 2014. “A Low Resolution Multi-camera System for Person Tracking.” In IEEE International Conference on Image Processing ICIP, 378–382. Piscataway, NJ, USA: IEEE.
Vancouver
1.
Eldib M, Nyan BB, Deboeverie F, Niño Castañeda J, Guan J, Van de Velde S, et al. A low resolution multi-camera system for person tracking. IEEE International Conference on Image Processing ICIP. Piscataway, NJ, USA: IEEE; 2014. p. 378–82.
IEEE
[1]
M. Eldib et al., “A low resolution multi-camera system for person tracking,” in IEEE International Conference on Image Processing ICIP, Paris, France, 2014, pp. 378–382.
@inproceedings{5671222,
  abstract     = {The current multi-camera systems have not studied the problem of person tracking under low resolution constraints. In this paper, we propose a low resolution sensor network for person tracking. The network is composed of cameras with a resolution of 30x30 pixels. The multi-camera system is used to evaluate probability occupancy mapping and maximum likelihood trackers against ground truth collected by ultra-wideband (UWB) testbed. Performance evaluation is performed on two video sequences of 30 minutes. The experimental results show that maximum likelihood estimation based tracker outperforms the state-of-the-art on low resolution cameras},
  author       = {Eldib, Mohamed and Nyan, Bo Bo and Deboeverie, Francis and Niño Castañeda, Jorge and Guan, Junzhi and Van de Velde, Samuel and Steendam, Heidi and Aghajan, Hamid and Philips, Wilfried},
  booktitle    = {IEEE International Conference on Image Processing ICIP},
  isbn         = {9781479957514},
  issn         = {1522-4880},
  keywords     = {Low resolution multi-camera systems,foreground detection,tracking,behavior analysis},
  language     = {eng},
  location     = {Paris, France},
  pages        = {378--382},
  publisher    = {IEEE},
  title        = {A low resolution multi-camera system for person tracking},
  year         = {2014},
}

Web of Science
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