行人 | 行人 a | 行人 b | 行人 c | 行人 d |
重叠率$\sigma $ | 20 | 50 | 10 | 25 |

Citation: Ying CHEN, Xiaoyue XU. Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190159

基于双向参考集矩阵度量学习的行人再识别
English
Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set
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-
[1]
FARENZENA M, BAZZANI L, PERINA A, et al. Person re-identification by symmetry-driven accumulation of local features[C]. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 2360–2367.
-
[2]
李幼蛟, 卓力, 张菁, 等. 行人再识别技术综述[J]. 自动化学报, 2018, 44(9): 1554–1568. doi: 10.16383/j.aas.2018.c170505
LI Youjiao, ZHUO Li, ZHANG Jing, et al. A survey of person re-identification[J]. Acta Automatica Sinica, 2018, 44(9): 1554–1568. doi: 10.16383/j.aas.2018.c170505 -
[3]
ZHENG Lilei, DUFFNER S, IDRISSI K, et al. Pairwise identity verification via linear concentrative metric learning[J]. IEEE Transactions on Cybernetics, 2018, 48(1): 324–335. doi: 10.1109/TCYB.2016.2634011
-
[4]
WANG Zheng, HU Ruimin, CHEN Chen, et al. Person reidentification via discrepancy matrix and matrix metric[J]. IEEE Transactions on Cybernetics, 2018, 48(10): 3006–3020. doi: 10.1109/TCYB.2017.2755044
-
[5]
CHEN Xiaojing, AN Le, and BHANU B. Reference set based appearance model for tracking across non-overlapping cameras[C]. 2013 International Conference on Distributed Smart Cameras, Palm Springs, USA, 2013: 1–6.
-
[6]
AN Le, KAFAI M, YANG Songfan, et al. Person reidentification with reference descriptor[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(4): 776–787. doi: 10.1109/TCSVT.2015.2416561
-
[7]
LIAO Shengcai and LI S Z. Efficient PSD constrained asymmetric metric learning for person re-identification[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3685–3693.
-
[8]
ZHONG Zhun, ZHENG Liang, CAO Donglin, et al. Re-ranking person re-identification with k-reciprocal encoding[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 3652–3661.
-
[9]
SUN Yipeng, TAO Xiaoming, LI Yang, et al. Robust two-dimensional principal component analysis via alternating optimization[C]. 2013 IEEE International Conference on Image Processing, Melbourne, Australia, 2013: 340–344.
-
[10]
GRAY D, BRENNAN S, and TAO Hai. Evaluating appearance models for recognition, reacquisition, and tracking[C]. The 10th IEEE International Workshop on Performance Evaluation for Tracking and Surveillance, Rio de Janeiro, 2007: 1–7.
-
[11]
ROTH P M, HIRZER M, KÖSTINGER M, et al. Mahalanobis distance learning for person re-identification[M]. GONG Shaogang, CRISTANI M, YAN Shuicheng, et al. Person Re-Identification. London: Springer, 2014: 247–267.
-
[12]
LI Wei, ZHAO Rui, and WANG Xiaogang. Human reidentification with transferred metric learning[C]. The 11th Asian Conference on Computer Vision, Daejeon, Korea, 2012: 31–44.
-
[13]
WANG Xiaogang, DORETTO G, SEBASTIAN T, et al. Shape and appearance context modeling[C]. 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007: 1–8.
-
[14]
MATSUKAWA T, OKABE T, SUZUKI E, et al. Hierarchical gaussian descriptor for person re-identification[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1363–1372.
-
[15]
MATSUKAWA T and SUZUKI E. Person re-identification using CNN features learned from combination of attributes[C]. The 23rd International Conference on Pattern Recognition, Cancun, Mexico, 2016: 2428–2433.
-
[16]
MIGNON A and JURIE F. PCCA: A new approach for distance learning from sparse pairwise constraints[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 2666–2672.
-
[17]
MA Bingpeng, SU Yu, and JURIE F. Bicov: A novel image representation for person re-identification and face verification[C]. British Machive Vision Conference, Surrey, UK, 2012: 57. 1–57.11.
-
[18]
KÖSTINGER M, HIRZER M, WOHLHART P, et al. Large scale metric learning from equivalence constraints[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 2288–2295.
-
[19]
ZHAO Rui, OUYANG Wanli, and WANG Xiaogang. Unsupervised salience learning for person re-identification[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3586–3593.
-
[20]
LI Zhen, CHANG Shiyu, LIANG Feng, et al. Learning locally-adaptive decision functions for person verification[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3610–3617.
-
[21]
ZHAO Rui, OUYANG Wanli, and WANG Xiaogang. Learning mid-level filters for person re-identification[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 144–151.
-
[22]
YANG Yang, YANG Jimei, YAN Junjie, et al. Salient color names for person re-identification[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 536–551.
-
[23]
LIAO Shengcai, HU Yang, ZHU Xiangyu, et al. Person re-identification by local maximal occurrence representation and metric learning[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 2197–2206.
-
[24]
AN Le, KAFAI M, YANG Songfan, et al. Person reidentification with reference descriptor[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(4): 776–787. doi: 10.1109/TCSVT.2015.2416561
-
[25]
YI Dong, LEI Zhen, LIAO Shengcai, et al. Deep metric learning for person re-identification[C]. 2014 International Conference on Pattern Recognition, Stockholm, Sweden, 2014: 34–39.
-
[26]
CHEN Shizhe, GUO Chaochun, and LAI Jianhuang. Deep ranking for person re-identification via joint representation learning[J]. IEEE Transactions on Image Processing, 2016, 25(5): 2353–2367. doi: 10.1109/TIP.2016.2545929
-
[27]
WANG Jin, WANG Zheng, GAO Changxin, et al. DeepList: Learning deep features with adaptive listwise constraint for person reidentification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(3): 513–524. doi: 10.1109/tcsvt.2016.2586851
-
[28]
DE CARVALHO PRATES R F and SCHWARTZ W R. CBRA: Color-based ranking aggregation for person re-identification[C]. 2015 IEEE International Conference on Image Processing, Quebec City, Canada, 2015: 1975–1979.
-
[29]
SHEN Yang, LIN Weiyao, YAN Junchi, et al. Person re-identification with correspondence structure learning[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3200–3208.
-
[30]
CHEN Yingcong, ZHENG Weishi, and LAI Jianhuang. Mirror representation for modeling view-specific transform in person re-identification[C]. The 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015: 3402–3408.
-
[31]
YAO Wenbin, WENG Zhenyu, and ZHU Yuesheng. Diversity regularized metric learning for person re-identification[C]. 2016 IEEE International Conference on Image Processing, Phoenix, USA, 2016: 4264–4268.
-
[32]
AHMED E, JONES M, and MARKS T K. An improved deep learning architecture for person re-identification[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 3908–3916.
-
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表 1 两个摄像头下参考集里的样本标签的重叠率$\sigma $ (%)
表 2 在3个数据集上采用不同特征的自身评估
方法 VIPeR CHUK01 PRID450S Rank-1 Rank-5 Rank-1 Rank-5 Rank-1 Rank-5 ${{\rm{L}}_{\rm{2}}}$范数(GoG) 19.00 38.00 24.17 51.33 32.44 60.00 F范数(GoG) 20.17 41.83 34.50 69.83 52.22 80.22 ${\rm{BR}}{{\rm{M}}^{\rm{2}}}{\rm{L}}$(GoG) 38.33 69.17 45.33 70.50 54.44 80.67 ${{\rm{L}}_{\rm{2}}}$范数(FCTNN) 29.00 46.00 37.44 58.00 31.73 57.96 F范数(FCTNN) 30.00 49.83 46.56 72.11 44.40 72.84 ${\rm{BR}}{{\rm{M}}^{\rm{2}}}{\rm{L}}$(FCTNN) 41.33 68.17 47.42 77.44 45.51 72.96 表 3 VIPeR数据集上的结果
方法 Rank-1 Rank-5 Rank-10 Rank-20 PCCA[16] 19.3 48.9 64.9 80.3 KISSME[18] 19.6 48.0 62.2 77.0 BiCov[17] 20.6 43.2 56.1 68.0 eSDC[19] 26.3 46.4 58.6 72.8 DeepMetric[25] 28.2 59.3 73.4 86.4 Midfilter[21] 29.1 52.5 65.9 79.9 LADF[20] 30.0 64.0 80.0 92.0 FTCNN[15]+XQDA 31.2 59.8 74.0 83.5 RD[24] 33.3 65.1 78.3 88.5 GoG[14]+XQDA 37.3 67.4 77.2 89.6 SCNCD[22] 37.8 68.5 81.2 90.4 ${\rm{D}}{{\rm{M}}^{\rm{3}}}$[4] 38.3 67.2 77.0 89.3 DeepRanking[26] 38.4 69.2 81.3 90.4 LOMO+XQDA[23] 40.0 68.5 80.5 91.0 DeepList[27] 40.5 69.1 80.1 91.2 ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(GoG) 38.33 69.17 81.50 89.50 ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(FTCNN) 41.33 68.17 82.00 90.33 表 4 PRID 450S数据集上的结果
表 5 CUHK01数据集上的结果
方法 Rank-1 Rank-5 Rank-10 Rank-20 SDALF[1] 9.9 22.6 30.3 41.0 TML[12] 20.0 43.5 56.0 69.3 MidFilter[21] 34.3 55.1 65.0 74.9 ImprovedDeep[32] 47.5 71.0 80.0 – RD[24] 31.1 – 68.5 79.1 ${\rm{D}}{{\rm{M}}^{\rm{3}}}$[4] 43.7 70.1 77.4 88.7 ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(GoG) 45.33 70.50 86.50 90.00 ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(FTCNN) 47.42 77.44 88.33 98.33 -