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Viewpoint Invariant Human Re-identification in Camera Networks Using Pose Priors and Subject-Discriminative Features. Ziyan Wu, Student Member, IEEE, Yang Li, Student Member, IEEE, and Richard J. Radke , Senior Member, IEEE

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  1. Viewpoint Invariant Human Re-identification inCamera Networks Using Pose Priors andSubject-Discriminative Features Ziyan Wu, Student Member, IEEE, Yang Li, Student Member, IEEE, and Richard J. Radke, Senior Member, IEEE IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, OCTOBER 2013

  2. Outline • Introduction • Related Work • Proposed Method • Experimental Results • Conclusion

  3. Introduction • Overlapping field of view

  4. Introduction • Non overlapping field of view: human identification problem

  5. Introduction • Difficulties: • Different camera viewpoint • Perspective distortion

  6. Related Work • Human identification methods: 1.Biometric method: Face[21], gait[46], silhouette[44] 2.Feature based: part based descriptor[4][10], SIFT[32], color histogram[13]

  7. [4] S. Bak, E. Corv ´ ee, F. Br ´ emond, and M. Thonnat. Person reidentification using spatial covariance regions of human body parts. AVSS, 2010 • [13] A. D’Angelo and J.-L. Dugelay. People re-identification in camera networks based on probabilistic color histograms. SPIE Electronic Imaging, 2011 • [10] L. Bourdev, S. Maji, and J. Malik. Describing people: A poseletbased approach to attribute classification. ICCV, 2011 • [21] M. Hirzer, C. Beleznai, P. M. Roth, and H. Bischof. Person reidentification by descriptive and discriminative classification. SCIA, 2011 • [32] D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60(2):91–110, Nov. 2004 • [44] D.-N. Truong Cong, L. Khoudour, C. Achard, C. Meurie, and O. Lezoray. People re-identification by spectral classification of silhouettes. Signal Process., 90(8):2362–2374, Aug. 2010 • [46] L. Wang, T. Tan, H. Ning, and W. Hu. Silhouette analysis based gait recognition for human identification. IEEE Trans.Pattern Anal. Mach. Intell., 25(12):1505–1518, Dec. 2003

  8. Proposed Method • Overview

  9. Proposed Method • Sub-image rectification:

  10. Proposed Method • View point angle

  11. Proposed Method • Pose prior:

  12. Proposed Method

  13. Proposed Method

  14. Proposed Method

  15. Experimental Results

  16. Conclusion • 1.Proposed a viewpoint variance identification method • 2.pose prior improve the performance • 3.It can be apply to surveillance systems

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