TRACKING-BY-REIDENTIFICATION IN A NON-OVERLAPPING FIELDS OF VIEW CAMERAS NETWORK

Boris Meden, Frédéric Lerasle, Patrick Sayd, Christophe Gabard

Abstract

This article tackles the problem of automatic multi-pedestrian tracking in non-overlapping fields of view camera networks, using monocular, uncalibrated cameras. Tracking is locally addressed by a Tracking-by- Detection and reidentification algorithm. We propose here to introduce the concept of global identity into a multi-target tracking algorithm, qualifying people at the network level, to allow us to rebound observation discontinuities. We embed that identity into the tracking loop thanks to the mixed-state particle filter framework, thus including it in the search space. Doing so, each tracker maintains a mutli-modality on the identity in the network of its target. We increase the decision strength introducing a high level decision scheme which integrates all the trackers hypothesis over all the cameras of the network with previous reidentification results and the topology of the network. The tracking and reidentification module is first tested with a single camera. We then evaluate the whole framework on a 3 non-overlapping fields of view network with 7 identities. The only a priori knowledge assumed is a topological map of the network.

References

  1. Bernardin, K. and Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: the clear mot metrics. Journal on Image and Video Processing.
  2. Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., and Van Gool, L. (2010). Online multiperson tracking-by-detection from a single, uncalibrated camera. Pattern Analysis and Machine Intelligence.
  3. Chen, K., Lai, C., Hung, Y., and Chen, C. (2008). An adaptive learning method for target tracking across multiple cameras. In Int. Conf. on Computer Vision and Pattern Recognition.
  4. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Int. Conf. on Computer Vision and Pattern Recognition.
  5. Farenzena, M., Bazzani, L., Perina, A., Murino, V., and Cristani, M. (2010). Person re-identification by symmetry-driven accumulation of local features. In Int. Conf. on Computer Vision and Pattern Recognition.
  6. Gray, D. and Tao, H. (2008). Viewpoint invariant pedestrian recognition with an ensemble of localized features. In Europ. Conf. on Computer Vision.
  7. Isard, M. and Blake, A. (1998). A mixed-state CONDENSATION tracker with automatic model-switching. In Int. Conf. on Computer Vision.
  8. Isard, M. and Blake, A. (2001). BraMBLe: a Bayesian multiple blob tracker. In Int. Conf. on Computer Vision.
  9. Kaucic, R., Perera, A., Brooksby, G., Kaufhold, J., and Hoogs, A. (2005). A unified framework for tracking through occlusions and accross sensor gaps. In Int. Conf. on Computer Vision and Pattern Recognition.
  10. Kuhn, H. (1955). The hungarian method for the assignment problem. Naval research logistics quarterly.
  11. Kuo, C., Huang, C., and Nevatia, R. (2010). Inter-camera association of multi-target tracks by on-line learned appearance affinity models. In Europ. Conf. on Computer Vision.
  12. Lev-Tov, A. and Moses, Y. (2010). Path recovery of a disappearing target in a large network of cameras. In Int. Conf. on Distributed Smart Cameras.
  13. Meden, B., Sayd, P., and Lerasle, F. (2011). Mixed-State Particle Filtering for Simultaneous Tracking and ReIdentification in Non-Overlapping Camera Networks. In Scandinavian Conference on Image Analysis.
  14. Okuma, K., Taleghani, A., De Freitas, N., Little, J., and Lowe, D. (2004). A boosted particle filter: multitarget detection and tracking. In Europ. Conf. on Computer Vision.
  15. Qu, W., Schonfeld, D., and Mohamed, M. (2007). Distributed bayesian multiple-target tracking in crowded environments using multiple collaborative cameras. Int. Journal EURASIP.
  16. Wojek, C., Roth, S., Schindler, K., and Schiele, B. (2010). Monocular 3D scene modeling and inferences: understanding multi-object traffic scenes. In Europ. Conf. on Computer Vision.
  17. Zajdel, W. and Kröse, B. (2005). A sequential bayesian algorithm for surveillance with nonoverlapping cameras. Int. Journal of Pattern Recognition and Artificial Intelligence.
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Paper Citation


in Harvard Style

Meden B., Lerasle F., Sayd P. and Gabard C. (2012). TRACKING-BY-REIDENTIFICATION IN A NON-OVERLAPPING FIELDS OF VIEW CAMERAS NETWORK . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-04-4, pages 95-103. DOI: 10.5220/0003818300950103


in Bibtex Style

@conference{visapp12,
author={Boris Meden and Frédéric Lerasle and Patrick Sayd and Christophe Gabard},
title={TRACKING-BY-REIDENTIFICATION IN A NON-OVERLAPPING FIELDS OF VIEW CAMERAS NETWORK},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={95-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003818300950103},
isbn={978-989-8565-04-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)
TI - TRACKING-BY-REIDENTIFICATION IN A NON-OVERLAPPING FIELDS OF VIEW CAMERAS NETWORK
SN - 978-989-8565-04-4
AU - Meden B.
AU - Lerasle F.
AU - Sayd P.
AU - Gabard C.
PY - 2012
SP - 95
EP - 103
DO - 10.5220/0003818300950103