An Human Perceptive Model for Person Re-identification

Angelo Cardellicchio, Tiziana D'Orazio, Tiziano Politi, Vito Renò

2015

Abstract

Person re-identification has increasingly become an interesting task in the computer vision field, especially after the well known terroristic attacks on the World Trade Center in 2001. Even if video surveillance systems exist since the early 1950s, the third generation of such systems is a relatively modern topic and refers to systems formed by multiple fixed or mobile cameras - geographically referenced or not - whose information have to be handled and processed by an intelligent system. In the last decade, researchers are focusing their attention on the person re-identification task because computers (and so video surveillance systems) can handle a huge amount of data reducing the time complexity of the algorithms. Moreover, some well known image processing techniques - i.e. background subtraction - can be embedded directly on cameras, giving modularity and flexibility to the whole system. The aim of this work is to present an appearance-based method for person re-identification that models the chromatic relationship between both different frames and different areas of the same frame. This approach has been tested against two public benchmark datasets (ViPER and ETHZ) and the experiments demonstrate that the person re-identification processing by means of intra frame relationships is robust and shows great results in terms of recognition percentage.

References

  1. Bak, S., Corvee, E., Brémond, F., and Thonnat, M. (2010). Person re-identification using spatial covariance regions of human body parts. In Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on, pages 435-440. IEEE.
  2. Belongie, S., Malik, J., and Puzicha, J. (2002). Shape matching and object recognition using shape contexts. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(4):509-522.
  3. Ess, A., Leibe, B., and Van Gool, L. (2007). Depth and appearance for mobile scene analysis. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1-8. IEEE.
  4. Farenzena, M., Bazzani, L., Perina, A., Murino, V., and Cristani, M. (2010). Person re-identification by symmetry-driven accumulation of local features. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2360-2367.
  5. Finlayson, G., Hordley, S., and Xu, R. (2005). Convex programming colour constancy with a diagonal-offset model. In Image Processing, 2005. ICIP 2005. IEEE International Conference on, volume 3, pages III948-51.
  6. Forssén, P.-E. (2007). Maximally stable colour regions for recognition and matching. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pages 1-8. IEEE.
  7. Gevers, T. and Smeulders, A. W. (1999). Color-based object recognition. Pattern recognition, 32(3):453-464.
  8. Gray, D. and Tao, H. (2008). Viewpoint invariant pedestrian recognition with an ensemble of localized features. In Computer Vision-ECCV 2008, pages 262- 275. Springer.
  9. Kviatkovsky, I., Adam, A., and Rivlin, E. (2013). Color invariants for person reidentification. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(7):1622-1634.
  10. Matsukawa, T., Okabe, T., and Sato, Y. (2014). Person reidentification via discriminative accumulation of local features. In Pattern Recognition (ICPR), 2014 IEEE Conference on.
  11. Swain, M. and Ballard, D. (1992). Indexing via color histograms. In Sood, A. and Wechsler, H., editors, Active Perception and Robot Vision, volume 83 of NATO ASI Series, pages 261-273. Springer Berlin Heidelberg.
  12. Truong Cong, D.-N., Khoudour, L., Achard, C., Meurie, C., and Lezoray, O. (2010). People re-identification by spectral classification of silhouettes. Signal Processing, 90(8):2362-2374.
  13. Van de Sande, K. E. A., Gevers, T., and Snoek, C. G. M. (2010). Evaluating color descriptors for object and scene recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(9):1582-1596.
  14. Yang, Y., Shengcai, L., Zhen, L., Dong, Y., and Li, S. Z. (2014). Color models and weighted covariance estimation for person re-identification. In Pattern Recognition (ICPR), 2014 IEEE Conference on.
  15. Zheng, W.-S., Gong, S., and Xiang, T. (2009). Associating groups of people. In Proceedings of the British Machine Vision Conference, pages 23.1-23.11. BMVA Press. doi:10.5244/C.23.23.
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Paper Citation


in Harvard Style

Cardellicchio A., D'Orazio T., Politi T. and Renò V. (2015). An Human Perceptive Model for Person Re-identification . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 638-643. DOI: 10.5220/0005341906380643


in Bibtex Style

@conference{visapp15,
author={Angelo Cardellicchio and Tiziana D'Orazio and Tiziano Politi and Vito Renò},
title={An Human Perceptive Model for Person Re-identification},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={638-643},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005341906380643},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - An Human Perceptive Model for Person Re-identification
SN - 978-989-758-089-5
AU - Cardellicchio A.
AU - D'Orazio T.
AU - Politi T.
AU - Renò V.
PY - 2015
SP - 638
EP - 643
DO - 10.5220/0005341906380643