Advertisement

People’s Re-identification Across Multiple Non-overlapping Cameras by Local Discriminative Patch Matching

  • Rabah IguernaissiEmail author
  • Djamal Merad
  • Pierre Drap
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10317)

Abstract

People’s tracking in multi-camera systems is one of the most important parts for the study of human’s behavior. In this work, we propose a re-identification method for associating people across non-overlapping cameras for tracking purposes. The proposed method is based on the use of discriminatives patches (salient regions). Our method is based on the proposal of a new framework that is used for selecting the most discriminative patches for each tracked individual. This framework is based on exploiting both appearance and spatial information to find the most discriminative salient regions. In this framework, each individual is represented by a set of values representing a rough description for several local patches extracted from the given individual. Then, this representation is used to select some interest patches that most represent the individual of interest compared to other individuals. At the end, these patches are used for associating new detected individuals to tracked ones.

Keywords

People re-identification Multi-camera tracking Salient regions 

References

  1. 1.
    Corvee, E., Bremond, F., Thonnat, M., et al.: Person re-identification using Haar-based and DCD-based signature. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8. IEEE (2010)Google Scholar
  2. 2.
    Zhao, R., Ouyang, W., Wang, X.: Learning mid-level filters for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 144–151 (2014)Google Scholar
  3. 3.
    Aziz, K.-E., Merad, D., Fertil, B.: People re-identification across multiple non-overlapping cameras system by appearance classification and silhouette part segmentation. In: 2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 303–308. IEEE (2011)Google Scholar
  4. 4.
    Martinel, N., Foresti, G.L.: Multi-signature based person re-identification. Electron. Lett. 48(13), 765–767 (2012)CrossRefGoogle Scholar
  5. 5.
    Bazzani, L., Cristani, M., Perina, A., Farenzena, M., Murino, V.: Multiple-shot person re-identification by HPE signature. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1413–1416. IEEE (2010)Google Scholar
  6. 6.
    Martinel, N., Micheloni, C., Foresti, G.L.: Kernelized saliency-based person re-identification through multiple metric learning. IEEE Trans. Image Process. 24(12), 5645–5658 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Zhao, R., Ouyang, W., Wang, X.: Person re-identification by salience matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2528–2535 (2013)Google Scholar
  8. 8.
    Datta, A., Brown, L.M., Feris, R., Pankanti, S.: Appearance modeling for person re-identification using weighted brightness transfer functions. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2367–2370. IEEE (2012)Google Scholar
  9. 9.
    Colombo, A., Orwell, J., Velastin, S.: Colour constancy techniques for re-recognition of pedestrians from multiple surveillance cameras. In: Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications-M2SFA2 2008 (2008)Google Scholar
  10. 10.
    Javed, O., Shaque, K., Rasheed, Z., Shah, M.: Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views. Comput. Vis. Image Underst. 109(2), 146–162 (2008)CrossRefGoogle Scholar
  11. 11.
    Martinel, N., Micheloni, C., Foresti, G.L.: A pool of multiple person re-identification experts. Pattern Recogn. Lett. 71, 23–30 (2016)CrossRefGoogle Scholar
  12. 12.
    Avraham, T., Lindenbaum, M.: Learning appearance transfer for person re-identification. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-Identification. ACVPR, pp. 231–246. Springer, London (2014). doi: 10.1007/978-1-4471-6296-4_11 CrossRefGoogle Scholar
  13. 13.
    Li, W., Wu, Y., Mukunoki, M., Minoh, M.: Common-near-neighbor analysis for person re-identification. In: 19th IEEE International Conference on Image Processing, pp. 1621–1624. IEEE (2012)Google Scholar
  14. 14.
    Zheng, W.-S., Gong, S., Xiang, T.: Re-identification by relative distance comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 653–668 (2013)CrossRefGoogle Scholar
  15. 15.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2197–2206 (2015)Google Scholar
  16. 16.
    Pedagadi, S., Orwell, J., Velastin, S., Boghossian, B.: Local fisher discriminant analysis for pedestrian re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3318–3325 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Aix-Marseille University, LSIS - UMR CNRS 7296Marseille Cedex 9France

Personalised recommendations