Local descriptors encoded by fisher vectors for person re-identification
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Enhanced local binary covariance matrices (ELBCM) for texture analysis and object tracking
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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Person re-identification is an important problem in computer vision, which involves matching appearance of individuals between non-overlapping camera views. In this paper we present a novel appearance-based method for person re-identification problem. Color feature, Gabor, local binary pattern (LBP) are utilized to form a covariance descriptor to handle the difficulties such as varying illumination, viewpoint angle and non-rigid body, then distances of these features are computed to match these individuals. Experimental results over the challenging dataset VIPeR demonstrate that our method obtains competitive performance.