Re-identification with RGB-D Sensors

People re-identification is a fundamental operation for any multi-camera surveillance scenario. Until now, it has been performed by exploiting primarily appearance cues, hypothesizing that the individuals cannot change their clothes. In this paper, we relax this constraint by presenting a set of 3D soft-biometric cues, being insensitive to appearance variations, that are gathered using RGB-D technology. The joint use of these characteristics provides encouraging performances on a benchmark of 79 people, that have been captured in different days and with different clothing. This promotes a novel research direction for the re-identification community, supported also by the fact that a new brand of affordable RGB-D cameras have recently invaded the worldwide market.

[1]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[2]  Alessandro Perina,et al.  Person re-identification by symmetry-driven accumulation of local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Rita Cucchiara,et al.  SARC3D: A New 3D Body Model for People Tracking and Re-identification , 2011, ICIAP.

[4]  Vittorio Murino,et al.  Custom Pictorial Structures for Re-identification , 2011, BMVC.

[5]  Yuan Yuan,et al.  Colour image coding with matching pursuit in the spatio-frequency domain , 2011 .

[6]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[7]  Zoltan-Csaba Marton,et al.  On fast surface reconstruction methods for large and noisy point clouds , 2009, 2009 IEEE International Conference on Robotics and Automation.

[8]  Edward Y. Chang,et al.  A video analysis framework for soft biometry security surveillance , 2005, VSSN '05.

[9]  Shaogang Gong,et al.  Person re-identification by probabilistic relative distance comparison , 2011, CVPR 2011.

[10]  Jean-Luc Dugelay,et al.  Person recognition using a bag of facial soft biometrics (BoFSB) , 2010, 2010 IEEE International Workshop on Multimedia Signal Processing.

[11]  Sadiye Guler,et al.  Automated person categorization for video surveillance using soft biometrics , 2010, Defense + Commercial Sensing.

[12]  Dieter Fox,et al.  Object recognition with hierarchical kernel descriptors , 2011, CVPR 2011.

[13]  Mubarak Shah,et al.  Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views , 2008, Comput. Vis. Image Underst..

[14]  Hai Tao,et al.  Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features , 2008, ECCV.

[15]  Zoltan-Csaba Marton,et al.  On Fast Surface Reconstruction Methods for Large and Noisy Datasets , 2009, IEEE International Conference on Robotics and Automation.

[16]  Jean-Luc Dugelay,et al.  Improving identification by pruning: A case study on face recognition and body soft biometric , 2012, 2012 13th International Workshop on Image Analysis for Multimedia Interactive Services.