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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Digital Image Processing
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Person Reidentification Using Spatiotemporal Appearance
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ViSE: Visual Search Engine Using Multiple Networked Cameras
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Fast support vector machine training and classification on graphics processors
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Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning Pairwise Dissimilarity Profiles for Appearance Recognition in Visual Surveillance
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Learning Discriminative Appearance-Based Models Using Partial Least Squares
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Re-identification Using Haar-based and DCD-based Signature
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Person Re-identification Using Spatial Covariance Regions of Human Body Parts
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Multi-pose Face Recognition for Person Retrieval in Camera Networks
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Matching Groups of People by Covariance Descriptor
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Multiple-Shot Person Re-identification by HPE Signature
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Detection of loitering individuals in public transportation areas
IEEE Transactions on Intelligent Transportation Systems
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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This paper presents an appearance-based model to address the human re-identification problem. Human re-identification is an important and still unsolved task in computer vision. In many systems there is a requirement to identify individuals or determine whether a given individual has already appeared over a network of cameras. The human appearance obtained in one camera is usually different from the ones obtained in another camera. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters. The paper focuses on a new appearance model based on Mean Riemannian Covariance (MRC) patches extracted from tracks of a particular individual. A new similarity measure using Riemannian manifold theory is also proposed to distinguish sets of patches belonging to a specific individual. We investigate the significance of MRC patches based on their reliability extracted during tracking and their discriminative power obtained by a boosting scheme. Our method is evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. Re-identification performance is presented using a cumulative matching characteristic (CMC) curve. We demonstrate that the proposed approach outperforms state of the art methods. Finally, the results of our approach are shown on two further and more pertinent datasets.