Consensus-based Inter-camera Re-identification - Across Non-overlapping Views

Fouad bousetouane, Cina Motamed, Lynda Dib

Abstract

Multi-object re-identification across cameras network with non-overlapping fields of view is a challenging problem. Firstly, the visual signature of the same object might be very different from one camera to another. Secondly, the blind zone between cameras creates the discontinuity in the observation of the same object in terms of locations and travelling times. Centralized inferences proposed in literature for inter-camera re-identification becomes insufficient in practice mostly with the requirement of real-time applications and dynamic cameras network. In this paper we present a completely distributed approach for inter-camera reidentification. The proposed approach based on the distributed inferences, where the set of smart-cameras collaborate to reach a consensus about the identities of objects circulating in the network. Local and global visual descriptors were combined into the proposed approach for inter-camera color mapping and invariant objects description. Experimental results of applying this approach show improvement in inter-camera reidentification and robustness in recovering from very complex conditions.

References

  1. Bousetouane, F., Dib, L., and Snoussi, H. (2011). Robust detection and tracking pedestrian object for real time surveillance applications. In SPIE, volume 8285, page 828508.
  2. Bousetouane, F., Dib, L., and Snoussi, H. (2012). Improved mean shift integrating texture and color features for robust real time object tracking. The Visual Computer J, Springer, pages 1-16.
  3. Chen, K.-W., Lai, C.-C., Lee, P.-J., Chen, C.-S., and Hung, Y.-P. (2011). Adaptive learning for target tracking and true linking discovering across multiple nonoverlapping cameras. Multimedia, IEEE Transactions on, 13(4):625 -638.
  4. Doretto, G., Sebastian, T., Tu, P., and Rittscher, J. (2011). Appearance-based person reidentification in camera networks: problem overview and current approaches. Journal of Ambient Intelligence and Humanized Computing, 2:127-151.
  5. Farenzena, M., Bazzani, L., Perina, A., Murino, V., and Cristani, M. (2010). Person re-identification by symmetry-driven accumulation of local features. In (CVPR),IEEE Conference on, pages 2360 -2367.
  6. Gilbert, A. and Bowden, R. (2006). Tracking objects across cameras by incrementally learning intercamera colour calibration and patterns of activity. In Computer Vision ECCV 2006, volume 3952 of LNCS, pages 125-136. Springer.
  7. Gray, D. and Tao, H. (2008). Viewpoint invariant pedestrian recognition with an ensemble of localized features. In Proceedings of the 10th ECCV, pages 262- 275. Springer.
  8. Javed, O., Shafique, K., Rasheed, Z., and Shah, M. (2008). Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views. Comput. Vis. Image Underst., 109(2):146-162.
  9. Meden, B., Sayd, P., and Lerasle, F. (2011). Mixed-state particle filtering for simultaneous tracking and reidentification in non-overlapping camera networks. In Image Analysis, LNCS, pages 124-133. Springer.
  10. Motamed, C. and Wallart, O. (2007). A temporal fusion strategy for cross-camera data association. Pattern Recognition Letters, 28(2):233-245.
  11. Olfati-Saber, R. and Sandell, N. (2008). Distributed tracking in sensor networks with limited sensing range. In American Control Conference, pages 3157 -3162.
  12. Porikli, F. and Divakaran, A. (2003). Multi-camera calibration, object tracking and query generation. In ICME 7803. Proceedings., volume 1, pages I - 653-6 vol.1.
  13. Prosser, B., Gong, S., and Xiang, T. (2008). Multi-camera matching using bi-directional cumulative brightness transfer functions. In Proceedings of the BMVC, pages 64.1-64.10. BMVA Press.
  14. Soto, C., Song, B., and Roy-Chowdhury, A. (2009). Distributed multi-target tracking in a self-configuring camera network. In CVPR 2009. IEEE Conference on, pages 1486 -1493.
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Paper Citation


in Harvard Style

bousetouane F., Motamed C. and Dib L. (2013). Consensus-based Inter-camera Re-identification - Across Non-overlapping Views . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 341-346. DOI: 10.5220/0004346803410346


in Bibtex Style

@conference{visapp13,
author={Fouad bousetouane and Cina Motamed and Lynda Dib},
title={Consensus-based Inter-camera Re-identification - Across Non-overlapping Views},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={341-346},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004346803410346},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - Consensus-based Inter-camera Re-identification - Across Non-overlapping Views
SN - 978-989-8565-48-8
AU - bousetouane F.
AU - Motamed C.
AU - Dib L.
PY - 2013
SP - 341
EP - 346
DO - 10.5220/0004346803410346