Efficient and robust shape matching for model based human motion capture
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
SARC3D: a new 3D body model for people tracking and re-identification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Part-based clothing segmentation for person retrieval
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Detection of loitering individuals in public transportation areas
IEEE Transactions on Intelligent Transportation Systems
The Vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Relaxed pairwise learned metric for person re-identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Towards person identification and re-identification with attributes
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Re-identification with RGB-D sensors
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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People re-identification is a challenging problem in surveillance and forensics and it aims at associating multiple instances of the same person which have been acquired from different points of view and after a temporal gap. Image-based appearance features are usually adopted but, in addition to their intrinsically low discriminability, they are subject to perspective and view-point issues. We propose to completely change the approach by mapping local descriptors extracted from RGB-D sensors on a 3D body model for creating a view-independent signature. An original bone-wise color descriptor is generated and reduced with PCA to compute the person signature. The virtual bone set used to map appearance features is learned using a recursive splitting approach. Finally, people matching for re-identification is performed using the Relaxed Pairwise Metric Learning, which simultaneously provides feature reduction and weighting. Experiments on a specific dataset created with the Microsoft Kinect sensor and the OpenNi libraries prove the advantages of the proposed technique with respect to state of the art methods based on 2D or non-articulated 3D body models.