Person Re-identification Results
[/trx_title]
This page shows results found in the literature for several person re-idenfication datasets (sort by rank-1 CMC). If you like to have your published results added in the following tables, please send an e-mail to Raphael Prates with the link (or the pdf) to your paper and the results to be reported. Up to now, we have tabulated the results for the following datasets.
- VIPeR dataset
- PRID 450S dataset
- CUHK 01 dataset
- ETHZ dataset (sequences #1, #2 and #3)
- iLIDS-VID dataset
- PRID-2011
We have released the codes that implement our works (see the list below). Some brief description, CMC-curves and the link to download are available in the following links.
Our Codes
- 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
VIPeR Dataset
| Method | r = 1 | r = 5 | r = 10 | r = 20 | r = 30 | year |
|---|---|---|---|---|---|---|
| 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 |
(*) We are sorry to inform that our previous results have a bug in the code. We update the results as well the code (we included the feature extraction).
[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
PRID 450S Dataset
| Method | Rank 1 | Rank 5 | Rank 10 | Rank 20 | Rank 30 | year |
|---|---|---|---|---|---|---|
| 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 |
(*) We are sorry to inform that our previous results have a bug in the code. We update the results as well the code (we included the feature extraction).
[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
CUHK 01 Dataset
| Method | Rank 1 | Rank 5 | Rank 10 | Rank 20 | Rank 30 | year |
|---|---|---|---|---|---|---|
| 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 |
(*) These methods used a single-shot evaluating protocol. They randomly selected one of the probe images to rank the gallery.
| 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 |
(*) These methods used a multi-shot evaluating protocol. They employed the two probe images available to compute the scores and rank the gallery images.
[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
ETHZ Dataset (Seq. #1)
| Method | Rank 1 | Rank 2 | Rank 3 | Rank 4 | Rank 5 | year |
|---|---|---|---|---|---|---|
| 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 |
ETHZ Dataset (Seq. #2)
| Method | Rank 1 | Rank 2 | Rank 3 | Rank 4 | Rank 5 | year |
|---|---|---|---|---|---|---|
| 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 |
ETHZ Dataset (Seq. #3)
| Method | Rank 1 | Rank 2 | Rank 3 | Rank 4 | Rank 5 | year |
|---|---|---|---|---|---|---|
| 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
| Method | r = 1 | r = 5 | r = 10 | r = 20 | r = 30 | year |
|---|---|---|---|---|---|---|
| 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)
| Method | r = 1 | r = 5 | r = 10 | r = 20 | r = 30 | year |
|---|---|---|---|---|---|---|
| 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
