·778· 智能系统学报 第12卷 nition.New York,USA,2006:1528-1535 re-identification[Cl//IEEE Conference on Computer Vis- [11]GRAY D,BRENNAN S,TAO H.Evaluating appearance ion and Pattern Recognition.Las vegas,USA,2016: models for recognition,reacquisition,and tracking[J].In- 1345-1353. ternational journal of computer vision,2007.89(2):56-68. [25]ZHAO R,OUYANG W,WANG X R.Unsupervised sali- [12]GONG S G,CRISTANI M,YAN S C,et al.Person re- ence learning for person re-identification[C]//IEEE Confer- identification[J].Advances in computer vision and pattern ence on Computer Vision and Pattern Recognition.Port- recognition,.2013,42(7):301-313. land,USA,2013:3586-3593. [13]YI D,LEI Z,LI S Z,Deep metric learning for practical [26]BAK S,CORVEE E,BREMOND F,et al.Person re-iden- person re-identification[C]//International Conference on tification using haar-based and DCD-based signature[C]// Pattern Recognition.Stockholm Waterfront,Sweden,2014. IEEE International Conference on Advanced Video and [14]OREOFEJ O,MEHRAN R,SHAH M.Human identity re- Signal Based Surveillance.Boston,USA,2010:1-8. cognition in aerial images[C]//IEEE Conference on Com- [27]KOESTINGER M,HIRZER M,WOHLHART P.et al puter Vision and Pattern Recognition.San Francisco,USA, Large scale metric learning from equivalence constraint 2010:709-716. [CV/IEEE Conference on Computer Vision and Pattern Re- [15]JUNGLING K,BODENSTEINER C,ARENS M.Person cognition.Providence,Rhode island,2012:2288-2295. re-identification in multi-camera networks[C]//Computer [28]ENGEL C,BAUMGARTNE P,HOLZMANN M.et al. Vision and Pattern Recognition Workshops.Colorado, Person re-identification by support vector ranking[C]// USA,2010:709-716. British Machine Vision Conference 2010.Aberystwyth. [16]ZHENG W S,GONG S G,XIANG T.Re identification by UK,2010:1-11 relative distance comparison[J].IEEE transactions on pat- [29]SCHWARTZ W R,DAVIS L S.Learning discriminative tern analysis and machine intelligence,2013,35(3):653. appearance-based models using partial least squares[C]// [17]PEDAGADI S,ORWELL J,VELASTIN S,et al.Local XXII Brazilian Symposium on Computer Graphics and Im- fisher discriminant analysis for pedestrian re-identification age Processing.Gramado,Brazil,2010:322-329 [C]//IEEE Conference on Computer Vision and Pattern Re- [30]LAYNE R,HOSPEDALES T M,GONG S G.Person Re- cognition.Portland,USA,2013:3318-3325. identification by Attributes[C]//The British Machine Vis- [18]CHEN Y C,ZHENG W S,LAI J H,et al.An asymmetric ion Conference.Nottingham,Park,2014,2(3):8. distance model for cross-view feature mapping in person [31]SHI Z,HOSPEDALSE T M,XIANG T.Transferring a se- re-identification[J].IEEE transactions on circuits and sys- mantic representation for person re-identification and tems for video technology,2016(99):1661-1675 search[C]//Computer Vision and Pattern Recognition.Bo- [19]XIONG F,GOU M,CAMPS O,et al.Person re-Identifica- ston,USA,2015:4184-4193. tion using kernel-based metric learning methods[C]// [32]MA B.SU Y,JURIE F.Local descriptors encoded by fish- European Conference on Computer Vision.Zurich, er vectors for person re-identification[C]//International Switzerland,2014:1-16. Conference on Computer Vision.Barcelona,Spain,2012: [20]ZHENG L,SHEN L,TIAN L,et al.Scalable person re- 413-422. identification:a benchmark[C]//IEEE International Confer- [33]CHEN D,YUAN Z,HUA G,et al.Similarity learning on ence on Computer Vision.Santiago,Chile,2015:1116- an explicit polynomial kernel feature map for person re- 1124. identification[C]//IEEE Conference on Computer Vision [21]WANG T,GONG S G,ZHU X,et al.Person re-identifica- and Pattern Recognition.Boston,USA,2015:1565-1573. tion by video ranking[C]//European Conference on Com- [34]GOU M.ZHANG X,RATES-BORRAS A.et al.Person puter Vision.Zurich,Switzerland,2014:688-703. re-identification in appearance impaired scenarios[C]// [22]ZHENG L,BIE Z,SUN Y,et al.MARS:A video bench- British Machine Vision Conference.[S.1.],2016:1-48 mark for large-scale person re-identification[M]//Euro- [35]KARANAM S,LI Y,RADKE R J.Person re-identifica- pean Conference on Computer Vision.Springer Interna- tion with discriminatively trained viewpoint invariant dic- tional Publishing,2016:868-884. tionaries[C]//IEEE International Conference on Computer [23]KLASER A,MARSZALEK M,SCHMID C.A spatio- Vision.Santiago,Chile,2015:4516-4524. temporal descriptor based on 3D-gradients[C]//British Ma- [36]SUGIYAMA,MASASHI.Local fisher discriminant ana- chine Vision Conference 2008.Nottingham,British.2008: lysis for supervised dimensionality reduction[J].Machine 152-159 learning,2010,78(1/2):35-61. [24]YOU J,WU A,LI X,et al.Top-push video-based person [37]MATSUKAWA T,OKABE T,SUZUKIE,et al.Hierarch-nition. New York, USA, 2006: 1528–1535. GRAY D, BRENNAN S, TAO H. Evaluating appearance models for recognition, reacquisition, and tracking[J]. In￾ternational journal of computer vision, 2007, 89(2): 56–68. [11] GONG S G, CRISTANI M, YAN S C, et al. Person re￾identification[J]. Advances in computer vision and pattern recognition, 2013, 42(7): 301–313. [12] YI D, LEI Z, LI S Z, Deep metric learning for practical person re-identification[C]//International Conference on Pattern Recognition. Stockholm Waterfront, Sweden, 2014. [13] OREOFEJ O, MEHRAN R, SHAH M. Human identity re￾cognition in aerial images[C]//IEEE Conference on Com￾puter Vision and Pattern Recognition. San Francisco, USA, 2010: 709–716. [14] JUNGLING K, BODENSTEINER C, ARENS M. Person re-identification in multi-camera networks[C]//Computer Vision and Pattern Recognition Workshops. Colorado, USA, 2010: 709–716. [15] ZHENG W S, GONG S G, XIANG T. Re identification by relative distance comparison[J]. IEEE transactions on pat￾tern analysis and machine intelligence, 2013, 35(3): 653. [16] PEDAGADI S, ORWELL J, VELASTIN S, et al. Local fisher discriminant analysis for pedestrian re-identification [C]//IEEE Conference on Computer Vision and Pattern Re￾cognition. Portland, USA, 2013: 3318–3325. [17] CHEN Y C, ZHENG W S, LAI J H, et al. An asymmetric distance model for cross-view feature mapping in person re-identification[J]. IEEE transactions on circuits and sys￾tems for video technology, 2016(99): 1661–1675. [18] XIONG F, GOU M, CAMPS O, et al. Person re-Identifica￾tion using kernel-based metric learning methods[C]// European Conference on Computer Vision. Zurich, Switzerland, 2014:1–16. [19] ZHENG L, SHEN L, TIAN L, et al. Scalable person re￾identification: a benchmark[C]//IEEE International Confer￾ence on Computer Vision. Santiago, Chile, 2015; 1116– 1124. [20] WANG T, GONG S G, ZHU X, et al. Person re-identifica￾tion by video ranking[C]//European Conference on Com￾puter Vision. Zurich, Switzerland, 2014: 688–703. [21] ZHENG L, BIE Z, SUN Y, et al. MARS: A video bench￾mark for large-scale person re-identification[M]//Euro￾pean Conference on Computer Vision. Springer Interna￾tional Publishing, 2016: 868–884. [22] KLASER A, MARSZALEK M, SCHMID C. A spatio￾temporal descriptor based on 3D-gradients[C]//British Ma￾chine Vision Conference 2008. Nottingham, British, 2008: 152–159. [23] [24] YOU J, WU A, LI X, et al. Top-push video-based person re-identification[C]//IEEE Conference on Computer Vis￾ion and Pattern Recognition. Las vegas, USA, 2016: 1345–1353. ZHAO R, OUYANG W, WANG X R. Unsupervised sali￾ence learning for person re-identification[C]//IEEE Confer￾ence on Computer Vision and Pattern Recognition. Port￾land, USA, 2013: 3586–3593. [25] BAK S, CORVEE E, BREMOND F, et al. Person re-iden￾tification using haar-based and DCD-based signature[C]// IEEE International Conference on Advanced Video and Signal Based Surveillance. Boston, USA, 2010: 1–8. [26] KOESTINGER M, HIRZER M, WOHLHART P, et al. Large scale metric learning from equivalence constraint [C]//IEEE Conference on Computer Vision and Pattern Re￾cognition. Providence, Rhode island, 2012: 2288–2295. [27] ENGEL C, BAUMGARTNE P, HOLZMANN M, et al. Person re-identification by support vector ranking[C]// British Machine Vision Conference 2010. Aberystwyth, UK, 2010: 1–11. [28] SCHWARTZ W R, DAVIS L S. Learning discriminative appearance-based models using partial least squares[C]// XXII Brazilian Symposium on Computer Graphics and Im￾age Processing. Gramado, Brazil, 2010: 322–329. [29] LAYNE R, HOSPEDALES T M, GONG S G. Person Re￾identification by Attributes[C]//The British Machine Vis￾ion Conference. Nottingham, Park, 2014, 2(3): 8. [30] SHI Z, HOSPEDALSE T M, XIANG T. Transferring a se￾mantic representation for person re-identification and search[C]//Computer Vision and Pattern Recognition. Bo￾ston, USA, 2015: 4184–4193. [31] MA B, SU Y, JURIE F. Local descriptors encoded by fish￾er vectors for person re-identification[C]//International Conference on Computer Vision. Barcelona, Spain, 2012: 413–422. [32] CHEN D, YUAN Z, HUA G, et al. Similarity learning on an explicit polynomial kernel feature map for person re￾identification[C]//IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015: 1565–1573. [33] GOU M, ZHANG X, RATES-BORRAS A, et al. Person re-identification in appearance impaired scenarios[C]// British Machine Vision Conference. [S.l.], 2016: 1–48. [34] KARANAM S, LI Y, RADKE R J. Person re-identifica￾tion with discriminatively trained viewpoint invariant dic￾tionaries[C]//IEEE International Conference on Computer Vision. Santiago, Chile, 2015: 4516–4524. [35] SUGIYAMA, MASASHI. Local fisher discriminant ana￾lysis for supervised dimensionality reduction[J]. Machine learning, 2010, 78(1/2): 35–61. [36] [37] MATSUKAWA T, OKABE T, SUZUKI E, et al. Hierarch- ·778· 智 能 系 统 学 报 第 12 卷