Resultados de Reidentificação de Pessoa
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Esta página mostra resultados encontrados na literatura para vários conjuntos de dados de identificação de pessoas (classificar por rank-1 CMC). Se você gostaria de ter seus resultados publicados adicionados nas tabelas a seguir, envie um e-mail para Raphael Prates com o link (ou o pdf) para o seu trabalho e os resultados a serem relatados. Até agora, tabulamos os resultados para os seguintes conjuntos de dados.
- VIPeR dataset
- PRID 450S dataset
- CUHK 01 dataset
- ETHZ dataset (sequences #1, #2 and #3)
- iLIDS-VID dataset
- PRID-2011
Nós publicamos os códigos que implementados nos nossos trabalhos (veja a lista abaixo). Uma breve descrição, curvas CMC e o link para download estão disponíveis nos links a seguir.
Nossos códigos
- Kernel Hierarchical PCA for Person Re-Identification (ICPR 2016)
- Kernel Partial Least Squares for Person Re-Identification (AVSS 2016)
- CBRA – Color-Based Ranking Aggregation for Person Re-Identification
- ICB 2015 Appearance-Based Person Re-Identification
Dataset VIPeR
Método | r = 1 | r = 5 | r = 10 | r = 20 | r = 30 | Ano |
---|---|---|---|---|---|---|
SCSP [38] | 53.5 | 82.6 | 91.5 | 96.6 | – | 2016 |
Kernel X-CRC [48] | 51.6 | 80.8 | 89.4 | 95.3 | 97.4 | 2016 |
FNN [35] | 51.1 | 81.0 | 91.4 | 96.9 | – | 2016 |
Cheng et al. [43] | 47.8 | 74.7 | 84.8 | 91.1 | 94.3 | 2016 |
LSSL [50] | 47.8 | 77.9 | 87.6 | 94.2 | – | 2016 |
Paisitkriangkrai et al. [2] | 44.9 | 76.3 | 88.2 | 94.9 | – | 2015 |
Chen et al. [3] | 43.0 | 75.2 | 87.3 | 94.8 | – | 2015 |
LSSCDL [39] | 42.7 | – | 84.3 | 91.9 | – | 2016 |
Zhang et al. [36] | 42.3 | 71.5 | 82.9 | 92.1 | – | 2016 |
NLML[41] | 42.3 | 71.0 | 85.2 | 94.2 | – | 2016 |
Shi et al. [4] | 41.6 | 71.9 | 86.2 | 95.1 | – | 2015 |
DRML [46] | 41.1 | – | 79.1 | 91.3 | – | 2016 |
MED_VL [49] | 41.1 | 71.7 | 83.2 | 91.7 | – | 2016 |
Ding et al. [5] | 40.5 | 60.8 | 70.4 | 84.4 | 90.9 | 2015 |
WARCA [40] | 40.2 | 68.2 | 80.7 | 91.1 | – | 2016 |
Liao et al. [6] | 40.0 | 68.0 | 80.5 | 91.1 | 95.5 | 2015 |
Kernel HPCA [44] | 39.4 | 73.0 | 85.1 | 93.5 | 96.1 | 2016 |
ECM [7] | 38.9 | 67.8 | 78.4 | 88.9 | – | 2015 |
Xiao et al. [37] | 38.6 | – | – | – | – | 2016 |
Deep Ranking [34] | 38.4 | 69.2 | 81.3 | 90.4 | 94.1 | 2016 |
Chen et al. [8] | 38.4 | 69.2 | 81.3 | 90.4 | – | 2015 |
SCNCD [9] | 37.8 | 68.5 | 81.2 | 90.4 | 94.2 | 2014 |
Yang et al. [10] | 37.6 | 68.1 | – | 90.2 | 94.0 | 2014 |
MKML [47] | 37.0 | 69.9 | 80.7 | 90.1 | – | 2016 |
RLML [11] | 35.3 | 67.4 | 81.6 | 90.8 | – | 2015 |
Ye et al. [12] | 35.0 | 62.9 | 72.0 | 82.5 | – | 2015 |
Shen et al. [13] | 34.8 | 68.7 | 82.3 | 91.8 | 94.9 | 2015 |
Ahmed et al. [14] | 34.8 | 63.5 | 75.0 | 80.0 | – | 2015 |
DRML [15] | 34.2 | – | 78.8 | 90.2 | – | 2015 |
Li et al. [16] | 34.0 | 64.2 | 77.5 | 88.6 | – | 2015 |
X-KPLS [45] | 33.1 | 67.7 | 81.0 | 90.8 | 94.2 | 2016 |
Prates and Schwartz [17] | 32.9 | 62.3 | 78.7 | 87.8 | 91.6 | 2015 |
Xiong et al. [18] | 32.3 | 65.8 | 79.7 | 90.9 | – | 2014 |
CBRA* [1] | 31.2 | 60.8 | 74.3 | 85.9 | 91.2 | 2015 |
Zeng et al. [19] | 31.2 | 56.0 | 70.0 | 82.0 | 86.5 | 2015 |
Ma et al. [20] | 31.1 | 58.3 | 70.7 | 82.4 | 89.9 | 2014 |
Zhao et al.[21] | 30.1 | 52.3 | 65.8 | – | – | 2013 |
LADF [22] | 30.0 | 64.0 | 80.0 | 92.0 | 96.5 | 2013 |
Zhang et al. [23] | 29.8 | 62.5 | 73.5 | – | – | 2014 |
Wang et al. [42] | 29.1 | 59.2 | 74.4 | 83.8 | – | 2016 |
Mid-Level Filters [24] | 29.1 | 52.5 | 65.9 | 79.9 | – | 2014 |
Region-based Salience [25] | 28.5 | 50.6 | 64.6 | 76.3 | – | 2015 |
Yi et al. [26] | 28.2 | 59.3 | 73.4 | 86.4 | 92.3 | 2014 |
KISSME [27] | 27.0 | 55.0 | 70.0 | 83.0 | 89.5 | 2012 |
Zhao et al. [28] | 26.7 | 50.7 | 62.4 | 76.4 | – | 2013 |
LDFV [29] | 26.5 | 56.4 | 70.9 | 84.6 | – | 2012 |
Leng et al. [30] | 23.8 | 53.2 | 70.2 | 84.0 | 90.4 | 2015 |
EIML [31] | 22.0 | 47.5 | 63.0 | 78.0 | 87.0 | 2014 |
SDALF [32] | 19.9 | 40.0 | 49.4 | 65.7 | 75.6 | 2010 |
PRDC [33] | 15.7 | 38.4 | 53.9 | 70.0 | – | 2011 |
(*) Lamentamos informar que nossos resultados anteriores apresentam um erro no código. Atualizamos os resultados e o código (incluímos a extração de recursos).
[1] Raphael Prates, William R. Schwartz, “CBRA – Color-Based Ranking Aggregation for Person Re-Identification,” in IEEE ICIP, 2015.
[2] Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel, “Learning to rank in person re-identification with metric ensembles,” in IEEE CVPR, 2015.
[3] Ying-Cong Chen, Wei-Shi Zheng, Jianhuang Lai, “Mirror Representation for Modeling View-Specific Transform in Person Re-Identification,” In IJCAI – International Joint Conference on Artificial Intelligence, 2015.
[4] Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang, “Transferring a Semantic Representation for Person Re-Identification and Search,” in IEEE CVPR 2015.
[5] Shengyong Ding, Liang Lin, Guangrun Wang, Hongyang Chao, “Deep feature learning with relative distance comparison for person re-identification.” Pattern Recognition, Volume 48, Issue 10, October 2015, Pages 2993-300.
[6] Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li. “Person Re-identification by Local Maximal Occurrence Representation and Metric Learning.” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
[7] Xiaokai Liu, Hongyu Wang, Yi Wu, Jimei Yang, and MingHsuan Yang, “An ensemble color model for human reidentification,” in IEEE Winter Conference on Applications of Computer Vision, 2015.
[8] Shi-Zhe Chen, Chun-Chao Guo, Jian-Huang Lai. Deep Ranking for Person Re-identification via Joint Representation Learning. arXiv, 2015.
[9] Yang Yang, Jimei Yang, Junjie Yan, Shengcai Liao, Dong Yi, and Stan Z Li, “Salient color names for person reidentification,” in ECCV, pp. 536–551. 2014.
[10] Yang Yang, Shengcai Liao, Zhen Lei, Dong Yi, and Stan Z. Li, “Color models and weighted covariance estimation for person re-identification,” in IAPR ICPR, Aug 2014, pp. 1874–1879.
[11] Venice Erin Liong, Jiwen Lu, Yongxin Ge, “Regularized local metric learning for person re-identification.” Pattern Recognition Letters, 2015.
[12] Mang Ye, Jun Chen, Qingming Leng, Chao Liang, Zheng Wang, and Kaimin Sun, “Coupled-view based ranking optimization for person re-identification.” 21st International Conference on Multimedia Modelling, MMM 2015.
[13] Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, “Person Re-identification with Correspondence Structure Learning.” IEEE International Conference on Computer Vision (ICCV), 2015.
[14] Ejaz Ahmed, Michael Jones, Tim K. Marks. ” An Improved Deep Learning Architecture for Person Re-Identification.” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3908-3916.
[15] Liong, V.E.,Yongxin Ge, Jiwen Lu, “Discriminative regularized metric learning for person re-identification,” inInternational Conference on Biometrics (ICB), 2015.
[16] Sheng Li, Ming Shao, Yun Fu. Cross-View Projective Dictionary Learning for Person Re-identification. In IJCAI – International Joint Conference on Artificial Intelligence, 2015.
[17] Raphael Prates, William R. Schwartz, “Appearance-Based Person Re-identification by Intra-Camera Discriminative Models and Rank Aggregation,” in International Conference on Biometrics (ICB), 2015.
[18] Fei Xiong, Mengran Gou, Octavia Camps, Mario Sznaier, “Person Re-Identification using Kernel-based Metric Learning Methods,” in ECCV 2014.
[19] Mingyong Zeng, Zemin Wu, Chang Tian, Lei Zhang, Lei Hu. “Efficient Person Re-identification by Hybrid Spatiogram and Covariance Descriptor.” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015, pp. 48-56.
[20] Bingpeng Ma, Yu Su, Frédéric Jurie. “Covariance descriptor based on bio-inspired features for person re-identification and face verification.” Image and Vision Computing, Volume 32, Issues 6–7, June–July 2014, Pages 379-390.
[21] Rui Zhao, Wanli Ouyang and Xiaogang Wang. “Person re-identification by salience matching.” IEEE International Conference on Computer Vision (ICCV), 2013.
[22] Zhen Li, Shiyu Chang, Feng Liang, Huang, T.S., Liangliang Cao and Smith, J.R. “Learning Locally-Adaptive Decision Functions for Person Verification.” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
[23] Ziming Zhang , Yuting Chen, Venkatesh Saligrama. A Novel Visual Word Co-occurrence Model for Person Re-identification. ECCV 2014.
[24] Rui Zhao, Wanli Ouyang, Xiaogang Wang. Learning Mid-level Filters for Person Re-identification. CVPR 2014.
[25] Yanbing Geng, Hai-Miao Hu, Guodong Zeng, Jin Zheng. “A person re-identification algorithm by exploiting region-based feature salience.” Journal of Visual Communication and Image Representation, Volume 29, May 2015, Pages 89-102.
[26] Dong Yi, Zhen Lei, Shengcai Liao, Stan Z. Li, “Deep Metric Learning for Person Re-identification“. International Conference on Pattern Recognition (ICPR), 2014, pp. 34-39.
[27] Martin Koestinger, Martin Hirzer, Paul Wohlhart, Peter M. Roth, and Horst Bischof, “Large scale metric learning from equivalence constraints,” in IEEE CVPR, 2012.
[28] Rui Zhao, Wanli Ouyang, Xiaogang Wang. “Unsupervised Salience Learning for Person Re-identification,” in IEEE CVPR, 2013.
[29] Bingpeng Ma, Yu Su, Frédéric Jurie. “Local Descriptors encoded by Fisher Vectors for Person Re-identification,” in ECCV Workshops and Demonstrations, 2012.
[30] Qingming Leng, Ruimin Hu, Chao Liang, Yimin Wang, Jun Chen, “Person re-identification with content and context re-ranking .” Multimedia Tools and Applications,, Volume 74, Issue 17, pp 6989-7014.
[31] M. Hirzer, P.M. Roth, and H. Bischof, “Person re-identification by efficient impostor-based metric learning,” in IEEE AVSS, Sept 2012, pp. 203–208.
[32] Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M. Person Re-Identification by Symmetry-Driven Accumulation of Local Features. CVPR 2010.
[33] Wei-Shi Zheng, Shaogang Gong, Tao Xiang. “Person re-identification by probabilistic relative distance comparison,” in Computer Vision and Pattern Recognition (CVPR), 2011.
[34] Chen, Shi-Zhe, Chun-Chao Guo, and Jian-Huang Lai. “Deep Ranking for Person Re-identification via Joint Representation Learning ” in Transactions on Image Processing (TIP) 2016
[35] Wu, S., Chen, Y. C., Li, X., Wu, A. C., You, J. J., & Zheng, W. S. “An enhanced deep feature representation for person re-identification ” in IEEE WACV 2016
[36] Zhang, Li., Xiang, T., Gong, S.“Learning a Discriminative Null Space for Person Re-identification ” in CVPR 2016
[37] Xiao, T., Li, H., Ouyang, W. and Wang, X. “Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification” in CVPR 2016
[38] Dapeng Chen, Zejian Yuan, Badong Chen, Nanning Zheng. “Similarity Learning with Spatial Constraints for Person Re-identification ” in CVPR 2016
[39] Ying Zhang, Baohua Li, Huchuan Lu, Atshushi Irie and Xiang Ruan “Sample-Specific SVM Learning for Person Re-identification ” in CVPR 2016
[40] Cijo Jose and François Fleuret. “Scalable Metric Learning viaWeighted Approximate Rank Component Analysis ” in CVPR 2016
[41] Siyuan Huang, Jiwen Lu, Jie Zhou, Anil K. Jain. “Nonlinear Local Metric Learning for Person Re-identification ” in CVPR 2016
[42] Wei Wang, Ali Taalimi, Kun Duan, Rui Guo and Hairong Qi. “Learning Patch-Dependent Kernel Forest for Person Re-Identification ” in WACV 2016
[43] De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, Nanning Zheng. “Person Re-Identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function ” in CVPR 2016
[44] Raphael Prates and William Robson Schwartz. “Kernel Hierarchical PCA for Person Re-Identification” in ICPR 2016
[45] Raphael Prates, Marina Oliveira and William Robson Schwartz. “Kernel Partial Least Squares for Person Re-Identification” in AVSS 2016
[46] Wenbin Yao, Zhenyu Weng, Yuesheng Zhu. ” Diversity Regularized Metric Learning For Person Re-Identification ” in ICIP 2016
[47] Muhammad Adnan Syed, Jianbin Jiao ” Multi-Kernel Metric Learning For Person Re-Identification ” in ICIP 2016
[48] Raphael Prates and William Robson Schwartz ” Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification ” in Arxiv 2016
[49] Yang Yang, Zhen Lei, Shifeng Zhang, Hailin Shi, Stan Z. Li ” Association for the Advancement of Artificial Intelligence ” AAAI 2016
[50] Yang Yang, Shengcai Liao, Zhen Lei, Stan Z. Li ” Large Scale Similarity Learning Using Similar Pairs for Person Verification ” AAAI 2016
Dataset PRID 450S
Método | Rank 1 | Rank 5 | Rank 10 | Rank 20 | Rank 30 | Ano |
---|---|---|---|---|---|---|
Kernel X-CRC [16] | 68.8 | 91.2 | 95.9 | 98.4 | 99.0 | 2016 |
FFN [10] | 66.6 | 86.8 | 92.8 | 96.9 | – | 2016 |
LSSCDL[11] | 60.5 | – | 88.6 | 93.6 | – | 2016 |
DRML [15] | 56.4 | – | 82.2 | 90.2 | – | 2016 |
Chen et al. [2] | 55.4 | 79.3 | 87.8 | 93.9 | – | 2015 |
X-KPLS [13] | 52.8 | 82.1 | 90.0 | 95.4 | 97.3 | 2016 |
Kernel HPCA [14] | 52.8 | 80.9 | 89.0 | 95.1 | 97.2 | 2016 |
MED_VL [17] | 45.9 | 73.0 | 82.9 | 91.1 | – | 2016 |
Shi et al. [3] | 44.9 | 71.7 | 77.5 | 86.7 | – | 2015 |
Shen et al. [4] | 44.4 | 71.6 | 82.2 | 89.8 | 93.3 | 2015 |
ECM [5] | 41.9 | 66.3 | 76.9 | 84.9 | – | 2015 |
SCNCD [7] | 41.6 | 68.9 | 79.4 | 87.8 | 95.4 | 2014 |
Yang et al. [6] | 40.6 | – | 80.7 | 90.0 | 92.4 | 2014 |
EIML [8] | 35.0 | 58.5 | 68.0 | 77.0 | 83.0 | 2012 |
KISSME [9] | 33.0 | 59.8 | 71.0 | 79.0 | 84.5 | 2012 |
CBRA* [1] | 26.4 | 57.1 | 71.0 | 83.2 | 88.8 | 2015 |
WARCA [12] | 24.6 | 55.5 | – | – | – | 2016 |
(*) Lamentamos informar que nossos resultados anteriores apresentam um erro no código. Atualizamos os resultados e o código (incluímos a extração de recursos).
[1] Raphael Prates, William R. Schwartz, “CBRA – Color-Based Ranking Aggregation for Person Re-Identification,” in IEEE ICIP, 2015.
[2] Ying-Cong Chen, Wei-Shi Zheng, Jianhuang Lai, “Mirror Representation for Modeling View-Specific Transform in Person Re-Identification,” In IJCAI – International Joint Conference on Artificial Intelligence, 2015.
[3] Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang, “Transferring a Semantic Representation for Person Re-Identification and Search,” in IEEE CVPR 2015.
[4] Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, “Person Re-identification with Correspondence Structure Learning.” IEEE International Conference on Computer Vision (ICCV), 2015.
[5] Xiaokai Liu, Hongyu Wang, Yi Wu, Jimei Yang, and MingHsuan Yang, “An ensemble color model for human reidentification,” IEEE Winter Conference on Applications of Computer Vision, 2015.
[6] Yang Yang, Shengcai Liao, Zhen Lei, Dong Yi, and Stan Z. Li, “Color models and weighted covariance estimation for person re-identification,” in IAPR ICPR, Aug 2014, pp. 1874–1879.
[7] Yang Yang, Jimei Yang, Junjie Yan, Shengcai Liao, Dong Yi, and Stan Z Li, “Salient color names for person reidentification,” in ECCV, pp. 536–551. 2014.
[8] M. Hirzer, P.M. Roth, and H. Bischof, “Person re-identification by efficient impostor-based metric learning,” in IEEE AVSS, Sept 2012, pp. 203–208.
[9] Martin Koestinger, Martin Hirzer, Paul Wohlhart, Peter M. Roth, and Horst Bischof, “Large scale metric learning from equivalence constraints,” in IEEE CVPR, 2012.
[10] Wu, S., Chen, Y. C., Li, X., Wu, A. C., You, J. J., & Zheng, W. S. An enhanced deep feature representation for person re-identification ” in IEEE WACV 2016
[11] Ying Zhang, Baohua Li, Huchuan Lu, Atshushi Irie and Xiang Ruan Sample-Specific SVM Learning for Person Re-identification ” in CVPR 2016
[12] Cijo Jose and François Fleuret. Scalable Metric Learning viaWeighted Approximate Rank Component Analysis ” in CVPR 2016
[13] Raphael Prates and William Robson Schwartz. ” Kernel Hierarchical PCA for Person Re-Identification ” in ICPR 2016
[14] Raphael Prates, Marina Oliveira and William Robson Schwartz. ” Kernel Partial Least Squares for Person Re-Identification ” in AVSS 2016
[15] Wenbin Yao, Zhenyu Weng, Yuesheng Zhu. “Diversity Regularized Metric Learning For Person Re-Identification ” in ICIP 2016
[16] Raphael Prates and William Robson Schwartz “Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification ” in Arxiv 2016
[17] Yang Yang, Zhen Lei, Shifeng Zhang, Hailin Shi, Stan Z. Li “Association for the Advancement of Artificial Intelligence ” AAAI 2016
Dataset CUHK 01
Método | Rank 1 | Rank 5 | Rank 10 | Rank 20 | Rank 30 | Ano |
---|---|---|---|---|---|---|
Xiao et al. [15] | 66.6 | – | – | – | – | 2016 |
LSSCDL [16] | 66.0 | – | – | – | – | 2016 |
WARCA [17] | 65.6 | 85.3 | 90.5 | 95.0 | – | 2016 |
Kernel X-CRC [20] | 61.2 | 80.9 | 87.3 | 93.2 | 95.6 | 2016 |
Li et al. [2] | 59.5 | 81.3 | 89.7 | 93.1 | – | 2015 |
Cheng et al. [18] | 53.7 | 84.3 | 91.0 | 96.3 | 98.3 | 2016 |
Paisitkriangkrai et al. [3] | 51.9 | 75.1 | 83.0 | 89.4 | – | 2015 |
Chen et al. [7] | 50.4 | 75.9 | 84.0 | 91.3 | – | 2015 |
Ahmed et al. [1] | 47.5 | 71.0 | 80.0 | – | – | 2015 |
Chen et al. [4] | 40.4 | 64.6 | 75.3 | 84.1 | – | 2015 |
Mid-Level Filters [9] | 34.3 | 55.1 | 65.0 | 74.9 | 80.3 | 2014 |
Zhao et al. [10] | 28.4 | 45.8 | 55.7 | 67.9 | 74.9 | 2013 |
Li et al. [8] | 20.0 | 43.5 | 56.0 | 69.3 | 77.3 | 2012 |
SDALF [11] | 9.9 | 22.6 | 30.3 | 41.0 | – | 2010 |
(*) Esses métodos usaram um protocolo de avaliação individual. Eles selecionaram aleatoriamente uma das imagens de teste para classificar a galeria.
Method | Rank 1 | Rank 5 | Rank 10 | Rank 20 | Rank 30 | year |
---|---|---|---|---|---|---|
Zhang et al. [14] | 65.0 | 85.0 | 89.9 | 94.4 | – | 2016 |
Liao et al. [5] | 63.2 | 84.0 | 90.0 | 93.7 | 96.0 | 2015 |
FFN [13] | 55.5 | 78.4 | 83.7 | 92.6 | – | 2016 |
Deep Ranking [12] | 50.4 | 75.9 | 84.1 | 91.3 | 99.3 | 2016 |
Zhang et al. [6] | 44.0 | 70.4 | 80.0 | – | – | 2014 |
MKML [19] | 31.2 | 57.9 | 70.7 | 81.4 | – | 2016 |
(*) Esses métodos usaram um protocolo de avaliação multi-shot. Eles empregaram as duas imagens de teste disponíveis para calcular as pontuações e classificar as imagens da galeria.
[1] Ejaz Ahmed, Michael Jones, Tim K. Marks. An Improved Deep Learning Architecture for Person Re-Identification. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3908-3916.
[2] Sheng Li, Ming Shao, Yun Fu. Cross-View Projective Dictionary Learning for Person Re-identification. In IJCAI – International Joint Conference on Artificial Intelligence, 2015.
[3] Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel. Learning to rank in person re-identification with metric ensembles. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
[4] Ying-Cong Chen, Wei-Shi Zheng, Jianhuang Lai. Mirror Representation for Modeling View-Specific Transform in Person Re-Identification. In IJCAI – International Joint Conference on Artificial Intelligence, 2015.
[5] Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li. Person Re-identification by Local Maximal Occurrence Representation and Metric Learning. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
[6] Ziming Zhang, Yuting Chen, Venkatesh Saligrama. A Novel Visual Word Co-occurrence Model for Person Re-identification. ECCV 2014.
[7] Shi-Zhe Chen, Chun-Chao Guo, Jian-Huang Lai. Deep Ranking for Person Re-identification via Joint Representation Learning. arXiv, 2015.
[8] Wei Li, Rui Zhao, Xiaogang Wang. Human Reidentification with Transferred Metric Learning. ACCV 2012.
[9] Rui Zhao, Wanli Ouyang, Xiaogang Wang. Learning Mid-level Filters for Person Re-identification. CVPR 2014.
[10] Rui Zhao, Wanli Ouyang, Xiaogang Wang. Person re-identification by salience matching. ICCV 2013.
[11] Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M. Person Re-Identification by Symmetry-Driven Accumulation of Local Features. CVPR 2010.
[12] Chen, Shi-Zhe, Chun-Chao Guo, and Jian-Huang Lai. Deep Ranking for Person Re-identification via Joint Representation Learning ” in Transactions on Image Processing (TIP) 2016
[13] Wu, S., Chen, Y. C., Li, X., Wu, A. C., You, J. J., & Zheng, W. S. An enhanced deep feature representation for person re-identification ” in IEEE WACV 2016
[14] Zhang, Li., Xiang, T., Gong, S.Learning a Discriminative Null Space for Person Re-identification ” in CVPR 2016
[15] Xiao, T., Li, H., Ouyang, W. and Wang, X. Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification” in CVPR 2016
[16] Ying Zhang, Baohua Li, Huchuan Lu, Atshushi Irie and Xiang Ruan Sample-Specific SVM Learning for Person Re-identification ” in CVPR 2016
[17] Cijo Jose and François Fleuret. Scalable Metric Learning viaWeighted Approximate Rank Component Analysis ” in CVPR 2016
[18] De Cheng, Yihong Gong, Sanping Zhou, Jinjun Wang, Nanning Zheng. “Person Re-Identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function ” in CVPR 2016
[19] Muhammad Adnan Syed, Jianbin Jiao ” Multi-Kernel Metric Learning For Person Re-Identification ” in ICIP 2016
[20] Raphael Prates and William Robson Schwartz ” Kernel Cross-View Collaborative Representation based Classification for Person Re-Identification ” in Arxiv 2016
Dataset ETHZ (Seq. #1)
Método | Rank 1 | Rank 2 | Rank 3 | Rank 4 | Rank 5 | Ano |
---|---|---|---|---|---|---|
Martinel et al. [1] | 84.0 | 88.0 | 91.0 | 93.0 | 94.0 | 2015 |
Ma et al. [2] | 83.0 | 87.0 | 90.0 | 91.0 | 92.0 | 2012 |
Zhao et al. [3] | 81.0 | 86.0 | 89.0 | 90.0 | 92.0 | 2013 |
Schwartz & Davis [4] | 79.0 | 85.0 | 86.0 | 87.0 | 88.0 | 2009 |
Martinel & Micheloni [5] | 78.0 | 83.0 | 87.0 | 88.0 | 89.0 | 2014 |
Hirzer et al. [6] | 78.0 | 84.0 | 87.0 | 89.0 | 90.0 | 2014 |
Hirzer et al. [7] | 77.0 | 83.0 | 87.0 | 90.0 | 91.0 | 2012 |
Ma et al. [8] | 74.0 | 80.0 | 83.0 | 85.0 | 87.0 | 2012 |
Avraham et al. [9] | 68.0 | 76.0 | 82.0 | 86.0 | 87.0 | 2012 |
SDALF [10] | 65.0 | 73.0 | 77.0 | 79.0 | 81.0 | 2010 |
Dataset ETHZ (Seq. #2)
Método | Rank 1 | Rank 2 | Rank 3 | Rank 4 | Rank 5 | Ano |
---|---|---|---|---|---|---|
Martinel et al. [1] | 81.0 | 86.0 | 90.0 | 93.0 | 95.0 | 2015 |
Zhao et al. [3] | 79.0 | 84.0 | 87.0 | 90.0 | 91.0 | 2013 |
Ma et al. [2] | 79.0 | 84.0 | 87.0 | 90.0 | 91.0 | 2012 |
Martinel & Micheloni [5] | 77.0 | 82.0 | 85.0 | 86.0 | 87.0 | 2014 |
Hirzer et al. [6] | 74.0 | 81.0 | 84.0 | 87.0 | 89.0 | 2014 |
Schwartz & Davis [4] | 74.0 | 79.0 | 81.0 | 83.0 | 84.0 | 2009 |
Ma et al. [8] | 71.0 | 79.0 | 83.0 | 86.0 | 88.0 | 2012 |
Avraham et al. [9] | 70.0 | 82.0 | 89.0 | 91.0 | 93.0 | 2012 |
Hirzer et al. [7] | 65.0 | 77.0 | 81.0 | 82.0 | 86.0 | 2012 |
SDALF [10] | 64.0 | 74.0 | 79.0 | 83.0 | 85.0 | 2010 |
Dataset ETHZ (Seq. #3)
Método | Rank 1 | Rank 2 | Rank 3 | Rank 4 | Rank 5 | Ano |
---|---|---|---|---|---|---|
Martinel et al. [1] | 91.0 | 97.0 | 99.0 | 99.0 | 99.0 | 2015 |
Hirzer et al. [6] | 91.0 | 95.0 | 97.0 | 98.0 | 98.0 | 2014 |
Ma et al. [2] | 91.0 | 94.0 | 96.0 | 97.0 | 97.0 | 2012 |
Avraham et al. [9] | 91.0 | 94.0 | 96.0 | 97.0 | 97.0 | 2012 |
Zhao et al. [3] | 90.0 | 95.0 | 96.0 | 97.0 | 98.0 | 2013 |
Hirzer et al. [7] | 83.0 | 90.0 | 92.0 | 94.0 | 96.0 | 2012 |
Ma et al. [8] | 82.0 | 87.0 | 90.0 | 92.0 | 93.0 | 2012 |
Martinel & Micheloni [5] | 81.0 | 85.0 | 89.0 | 93.0 | 94.0 | 2014 |
Schwartz & Davis [4] | 77.0 | 81.0 | 82.0 | 84.0 | 85.0 | 2009 |
SDALF [10] | 76.0 | 83.0 | 86.0 | 88.0 | 90.0 | 2010 |
[1] Martinel, N., Das, A. , Micheloni, C., Roy-Chowdhury, A.K. Re-Identification in the Function Space of Feature Warps. PAMI, 2015.
[2] Bingpeng Ma, Yu Su, Frédéric Jurie. Local Descriptors Encoded by Fisher Vectors for Person Re-identification. ECCV 2012.
[3] Rui Zhao, Wanli Ouyang, Xiaogang Wang. Unsupervised Salience Learning for Person Re-identification. CVPR 2013.
[4] Schwartz, W.R., Davis, L.S. Learning discriminative appearance-based models using partial least squares. SIBGRAPi 2009.
[5] Niki Martinel, Christian Micheloni. Sparse Based Matching of Random Patches for Person Re-Identification. ICDSC, 2014.
[6] Hirzer, M., Roth, P.M., Bischof, H. Person re-identification by efficient impostor-based metric learning. AVSS 2012.
[7] Martin Hirzer, Peter M. Roth, Martin Köstinger, Horst Bischof. Relaxed Pairwise Learned Metric for Person Re-identification. ECCV 2012.
[8] Bingpeng Ma, Yu Su and Frederic Jurie. BiCov: a novel image representation for person re-identification and face verification. BMVC 2012.
[9] Tamar Avraham, Ilya Gurvich, Michael Lindenbaum, Shaul Markovitch. Learning Implicit Transfer for Person Re-identification. ECCV 2012.
[10] Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M. Person Re-Identification by Symmetry-Driven Accumulation of Local Features. CVPR 2010.
iLIDS-VID
Método | r = 1 | r = 5 | r = 10 | r = 20 | r = 30 | Ano |
---|---|---|---|---|---|---|
McLaughlin et al. [1] | 58 | 84 | 91 | 96 | – | 2016 |
TDL [2] | 56.3 | 87.6 | 95.6 | 98.3 | – | 2016 |
TAPR [3] | 55.0 | 87.5 | 93.8 | 97.2 | – | 2016 |
SI²DL [5] | 48.7 | 81.1 | 89.2 | 97.3 | – | 2016 |
DRML [6] | 43.1 | – | 72.7 | 82 | – | 2016 |
FAST3D [4] | 28.4 | 54.7 | 66.7 | 78.1 | – | 2016 |
[1] McLaughlin, N., Martinez del Rincon, J., & Miller, P. Recurrent Convolutional Network for Video-based Person Re-Identification ” in CVPR 2016
[2] Jinjie You, Ancong Wu, Xiang Li and Wei-Shi Zheng. Top-push Video-based Person Re-identification ” in CVPR 2016
[3] Changxin Gao, Jin Wang, Leyuan Liu, Jin-Gang Yu, and Nong Sang Temporally Aligned Pooling Representation for Video-Based Person Re-Identification ” in ICIP 2016
[4] Changxin Gao, Jin Wang, Leyuan Liu, Jin-Gang Yu, and Nong Sang Temporally Aligned Pooling Representation for Video-Based Person Re-Identification ” in ICIP 2016
[5] Xiaoke Zhu, Xiao-Yuan Jing, Fei Wu, Hui Feng Video-Based Person Re-Identification by Simultaneously Learning Intra-Video and Inter-Video Distance Metrics ” in IJCAI 2016
[6] Wenbin Yao, Zhenyu Weng, Yuesheng Zhu. ” Diversity Regularized Metric Learning For Person Re-Identification ” in ICIP 2016
PRID-2011 (Multi-shot)
Método | r = 1 | r = 5 | r = 10 | r = 20 | r = 30 | Ano |
---|---|---|---|---|---|---|
SI²DL [5] | 76.7 | 95.6 | 96.7 | 98.9 | – | 2016 |
McLaughlin et al. [1] | 70 | 90 | 95 | 97 | – | 2016 |
TAPR [3] | 68.6 | 94.6 | 97.4 | 98.9 | – | 2016 |
TDL [2] | 56.7 | 80.0 | 87.6 | 93.4 | – | 2016 |
FAST3D [4] | 31.1 | 60.3 | 76.4 | 88.6 | – | 2016 |
[1] McLaughlin, N., Martinez del Rincon, J., & Miller, P. Recurrent Convolutional Network for Video-based Person Re-Identification ” in CVPR 2016
[2] Jinjie You, Ancong Wu, Xiang Li and Wei-Shi Zheng. Top-push Video-based Person Re-identification ” in CVPR 2016
[3] Changxin Gao, Jin Wang, Leyuan Liu, Jin-Gang Yu, and Nong Sang Temporally Aligned Pooling Representation for Video-Based Person Re-Identification ” in ICIP 2016
[4] Xiaoke Zhu, Xiao-Yuan Jing, Fei Wu, Hui Feng A Fast Adaptive Spatio-temporal 3D Feature For Video-Based Person Re-identification ” in ICIP 2016
[5] Xiaoke Zhu, Xiao-Yuan Jing, Fei Wu, Hui Feng Video-Based Person Re-Identification by Simultaneously Learning Intra-Video and Inter-Video Distance Metrics ” in IJCAI 2016