Dense Invariant Feature-Based Support Vector Ranking for Cross-Camera Person Reidentification

Recently, support vector ranking (SVR) has been adopted to address the challenging person reidentification problem. However, the ranking model based on ordinary global features cannot well represent the significant variation of pose and viewpoint across camera views. To address this issue, a novel ranking method that fuses the dense invariant features (DIFs) is proposed in this paper to model the variation of images across camera views. An optimal space for ranking is learned by simultaneously maximizing the margin and minimizing the error on the fused features. The proposed method significantly outperforms the original SVR algorithm due to the invariance of the DIFs, the fusion of the bidirectional features, and the adaptive adjustment of parameters. Experimental results demonstrate that the proposed method is competitive with state-of-the-art methods on two challenging data sets, showing its potential for real-world person reidentification.