Viewpoint Invariant Human Re-identification inCamera 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

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, OCTOBER 2013

Outline

• Introduction

• Related Work

• Proposed Method

• Experimental Results

• Conclusion

Introduction

• Overlapping field of view

Introduction

• Non overlapping field of view: human identification problem

Introduction

• Difficulties:

• Different camera viewpoint

• Perspective distortion

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]

• [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

Proposed Method

• Overview

Proposed Method

• Sub-image rectification:

Proposed Method

• View point angle

Proposed Method

• Pose prior:

Proposed Method

Proposed Method

Proposed Method

Experimental Results

Conclusion

• 1.Proposed a viewpoint variance identification method

• 2.pose prior improve the performance

• 3.It can be apply to surveillance systems