Person Re-identification by Bidirectional Learning of Sample-Specific SVMs

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Authors Ying Zhang, Baohua Li, Huchuan Lu, Atsushi Irie, Xiang Ruan
Journal/Conference Name CVPR2016
Paper Category
Paper Abstract Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics. However,most existing approaches focus on learning a fixed distance metric for all instance pairs, while ignoring the individuality of each person. In this paper, we formulate the person re-identification problem as an imbalanced classification problem and learn a classifier specifically for each pedestrian such that the matching model is highly tuned to the individual’s appearance. To establish correspondence be-tween feature space and classifier space, we propose a Least Square Semi-Coupled Dictionary Learning (LSSCDL) algorithm to learn a pair of dictionaries and a mapping function efficiently. Extensive experiments on a series of challenging databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches,especially on the rank-1 recognition rate.
Date of publication 2016
Code Programming Language MATLAB
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