Panoramic Appearance Map (PAM) for Multi-camera Based Person Re-identification
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Computer Vision and Image Understanding
A real-time full body tracking and humanoid animation system
Parallel Computing
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Cascade of descriptors to detect and track objects across any network of cameras
Computer Vision and Image Understanding
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Probabilistic people tracking with appearance models and occlusion classification: The AD-HOC system
Pattern Recognition Letters
3DPeS: 3D people dataset for surveillance and forensics
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
People orientation recognition by mixtures of wrapped distributions on random trees
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Relaxed pairwise learned metric for person re-identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Re-identification with RGB-D sensors
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Learning articulated body models for people re-identification
Proceedings of the 21st ACM international conference on Multimedia
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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We propose a newsimplified 3Dbody model (called SARC3D) for surveillance application, which can be created, updated and compared in real-time. People are detected and tracked in each calibrated camera, with their silhouette, appearance, position and orientation extracted and used to place, scale and orientate a 3D body model. For each vertex of the model a signature (color features, reliability and saliency) is computed from 2D appearance images and exploited for matching. This approach achieves robustness against partial occlusions, pose and viewpoint changes. The complete proposal and a full experimental evaluation are presented, using a new benchmark suite and the PETS2009 dataset.