TY - JOUR
T1 - Dense Invariant Feature-Based Support Vector Ranking for Cross-Camera Person Reidentification
AU - Tan, Shoubiao
AU - Zheng, Feng
AU - Liu, Li
AU - Han, Jungong
AU - Shao, Ling
PY - 2018/12/13
Y1 - 2018/12/13
N2 - 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.
AB - 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.
KW - Dense invariant feature (DIF)
KW - feature fusion
KW - person reidentification
KW - support vector ranking (SVR)
U2 - 10.1109/TCSVT.2016.2555739
DO - 10.1109/TCSVT.2016.2555739
M3 - Article
AN - SCOPUS:85041945469
SN - 1051-8215
VL - 28
SP - 356
EP - 363
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
IS - 2
M1 - 7456248
ER -