DETECTION AND LOCALISATION OF STATIONARY OBJECTS WITH A PAIR OF PTZ CAMERAS

Constant Guillot, Quoc-Cuong Pham, Patrick Sayd, Christophe Tilmant, Jean-Marc Lavest

Abstract

We propose a novel approach for detecting and localising stationary objects using a pair of PTZ cameras monitoring a wide scene. First we propose a stationary object detection and labelling algorithm. It relies on the re-identification of foreground blocks of the image and an MRF framework to detect and separate the stationary objects of the scene. Second we propose a geometric approach for robustly matching the detected silhouettes of stationary objects from a pair of PTZ cameras. Our system is tested on challenging sequences which prove its robustness to occlusions even in an unknown non planar 3D scene.

References

  1. Alahari, K., Kohli, P., and Torr, P. H. S. (2008). Reduce, reuse & recycle: Efficiently solving multi-label MRFs. In CVPR.
  2. Bayona, A., SanMiguel, J., and Martinez, J. (2009). Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques. In AVSS.
  3. Bayona, A., SanMiguel, J., and Martinez, J. (2010). Stationary foreground detection using background subtraction and temporal difference in video surveillance. In ICIP.
  4. Beynon, M. D., Van Hook, D. J., Seibert, M., Peacock, A., and Dudgeon, D. (2003). Detecting abandoned packages in a multi-camera video surveillance system. In AVSS.
  5. Cipolla, R., Astrom, K., and Giblin, P. (1995). Motion from the frontier of curved surfaces. In ICCV.
  6. Fleuret, F., Berclaz, J., Lengagne, R., and Fua, P. (2008). Multicamera people tracking with a probabilistic occupancy map. PAMI.
  7. Guillot, C., Taron, M., Sayd, P., Pham, Q.-C., Tilmant, C., and Lavest, J.-M. (2010). Background subtraction for ptz cameras performing a guard tour and application to cameras with very low frame rate. In ACCV VS.
  8. Guler, S., Silverstein, J., and Pushee, I. (2007). Stationary objects in multiple object tracking. In AVSS.
  9. Khan, S. M. and Shah, M. (2009). Tracking multiple occluding people by localizing on multiple scene planes. PAMI.
  10. Liao, H.-H., Chang, J.-Y., and Chen, L.-G. (2008). A localized approach to abandoned luggage detection with foreground-mask sampling. In AVSS.
  11. Mathew, R., Yu, Z., and Zhang, J. (2005). Detecting new stable objects in surveillance video. In Multimedia Signal Processing, Workshop on.
  12. Miezianko, R. and Pokrajac, D. (2008). Localization of detected objects in multi-camera network. In ICIP.
  13. Porikli, F., Ivanov, Y., and Haga, T. (2008). Robust abandoned object detection using dual foregrounds. EURASIP J. Adv. Signal Process, 2008.
  14. Utasi, A. and Csaba, B. (2010). Multi-camera people localization and height estimation using multiple birth and death dynamics. In ACCV VS.
Download


Paper Citation


in Harvard Style

Guillot C., Pham Q., Sayd P., Tilmant C. and Lavest J. (2012). DETECTION AND LOCALISATION OF STATIONARY OBJECTS WITH A PAIR OF PTZ CAMERAS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 591-596. DOI: 10.5220/0003827205910596


in Bibtex Style

@conference{visapp12,
author={Constant Guillot and Quoc-Cuong Pham and Patrick Sayd and Christophe Tilmant and Jean-Marc Lavest},
title={DETECTION AND LOCALISATION OF STATIONARY OBJECTS WITH A PAIR OF PTZ CAMERAS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={591-596},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003827205910596},
isbn={978-989-8565-03-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2012)
TI - DETECTION AND LOCALISATION OF STATIONARY OBJECTS WITH A PAIR OF PTZ CAMERAS
SN - 978-989-8565-03-7
AU - Guillot C.
AU - Pham Q.
AU - Sayd P.
AU - Tilmant C.
AU - Lavest J.
PY - 2012
SP - 591
EP - 596
DO - 10.5220/0003827205910596