Graph Regularized and Label-matched Dictionary Learning for Video-based Person Re-identification

摘要

In recent years, video-based person re-identification has attracted more and more attention. However, most existing video-based methods do not fully consider the intrinsic structure and invariant information of the same person across different cameras. In this paper, we propose a graph regularized and label-matched dictionary learning (GRLDL) method to capture the intrinsic structure of the same person between two cameras. Firstly, in order to reduce the variations between different cameras, we use local Fisher discriminant analysis to transform the person videos from different cameras into a common feature space. A dictionary is learned from this common space. Then, we construct a graph regularization term to preserve the geometrical structure of the same person and enhance the discriminative ability of the learned dictionary. Finally, a projective matrix is introduced to map the coding coefficients into a label space, which is able to correlate and match the same person under different cameras. Experiments on the public iLIDS-VID and PRID 2011 datasets show the effectiveness of the proposed method.