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Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates.
Inter-camera color calibration by correlation model function
F. Porikli · 2003
Earlier work this paper cites.
Keeping track of humans: Have i seen this person before?
W. Zajdel, Z. Zivkovic, and B. Krose · 2005
Earlier work this paper cites.
Person reidentification using spatiotemporal appearance
N. Gheissari, T. B. Sebastian, and R. Hartley · 2006
Earlier work this paper cites.
Evaluating appearance models for recognition, reacquisition, and tracking
D. Gray, S. Brennan, and H. Tao · 2007
Earlier work this paper cites.
Shape and appearance context modeling
X. Wang, G. Doretto, T. Sebastian, J. Rittscher, and P. Tu · 2007
Earlier work this paper cites.
Viewpoint invariant pedestrian recognition with an ensemble of localized features
D. Gray and H. Tao · 2008
Earlier work this paper cites.
Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences
O. Hamdoun, F. Moutarde, B. Stanciulescu, and B. Steux · 2008
Earlier work this paper cites.
Multi-camera activity correlation analysis
C. C. Loy, T. Xiang, and S. Gong · 2009
Earlier work this paper cites.
Associating groups of people
W.-S. Zheng, S. Gong, and T. Xiang · 2009
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
Earlier work this paper cites.
Person re-identification by support vector ranking
B. Prosser, W.-S. Zheng, S. Gong, T. Xiang, and Q. Mary · 2010
Earlier work this paper cites.
Person re-identification by descriptive and discriminative classification
M. Hirzer, C. Beleznai, P. M. Roth, and H. Bischof · 2011
Earlier work this paper cites.
Person re-identification by probabilistic relative distance comparison
W.-S. Zheng, S. Gong, and T. Xiang · 2011
Earlier work this paper cites.
Large scale metric learning from equivalence constraints
M. Koestinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof · 2012
Earlier work this paper cites.
Locally aligned feature transforms across views
W. Li and X. Wang · 2013
Earlier work this paper cites.
Intelligent multi-camera video surveillance: A review
X. Wang · 2013
Earlier work this paper cites.
Harry potter’s marauder’s map: Localizing and tracking multiple persons-of-interest by nonnegative discretization
S.-I. Yu, Y. Yang, and A. Hauptmann · 2013
Cited alongside, same era.
Unsupervised salience learning for person re-identification
R. Zhao, W. Ouyang, and X. Wang · 2013
Cited alongside, same era.
Fast feature pyramids for object detection
P. Dollár, R. Appel, S. Belongie, and P. Perona · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
Deepreid: Deep filter pairing neural network for person re-identification
W. Li, R. Zhao, T. Xiao, and X. Wang · 2014
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2014
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Later among the works it cites.
Person re-identification with correspondence structure learning
Y. Shen, W. Lin, J. Yan, M. Xu, J. Wu, and J. Wang · 2015
Later among the works it cites.
Pedestrian detection aided by deep learning semantic tasks
Y. Tian, P. Luo, X. Wang, and X. Tang · 2015
Later among the works it cites.
Convolutional channel features
B. Yang, J. Yan, Z. Lei, and S. Z. Li · 2015
Later among the works it cites.
Filtered channel features for pedestrian detection
S. Zhang, R. Benenson, and B. Schiele · 2015
Later among the works it cites.
Scalable person re-identification: A benchmark
L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian · 2015
Later among the works it cites.
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Cited alongside, same era.
Person search in a scene by jointly modeling people commonness and person uniqueness
Y. Xu, B. Ma, R. Huang, and L. Lin · 2014
Cited alongside, same era.
An improved deep learning architecture for person re-identification
E. Ahmed, M. Jones, and T. K. Marks · 2015
Cited alongside, same era.
Learning complexity-aware cascades for deep pedestrian detection
Z. Cai, M. Saberian, and N. Vasconcelos · 2015
Cited alongside, same era.
Deep feature learning with relative distance comparison for person re-identification
S. Ding, L. Lin, G. Wang, and H. Chao · 2015
Cited alongside, same era.
Fast r-cnn
R. Girshick · 2015
Cited alongside, same era.
Taking a deeper look at pedestrians
J. Hosang, M. Omran, R. Benenson, and B. Schiele · 2015
Cited alongside, same era.
Partial person re-identification
W.-S. Zheng, X. Li, T. Xiang, S. Liao, J. Lai, and S. Gong · 2015
Later among the works it cites.
Person re-identification by multi-channel parts-based cnn with improved triplet loss function
D. Cheng, Y. Gong, S. Zhou, J. Wang, and N. Zheng · 2016
Closest in time.
Structured feature learning for pose estimation
X. Chu, W. Ouyang, H. Li, and X. Wang · 2016
Closest in time.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Closest in time.
Performance measures and a data set for multi-target, multi-camera tracking
E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi · 2016
Closest in time.
Temporal segment networks: towards good practices for deep action recognition
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool · 2016
Closest in time.
Learning deep feature representations with domain guided dropout for person re-identification
T. Xiao, H. Li, W. Ouyang, and X. Wang · 2016
Closest in time.
Is faster r-cnn doing well for pedestrian detection?
L. Zhang, L. Lin, X. Liang, and K. He · 2016
Closest in time.
Person re-identification in the wild
L. Zheng, H. Zhang, S. Sun, M. Chandraker, and Q. Tian · 2016
Closest in time.
Unlabeled samples generated by gan improve the person re-identification baseline in vitro
Z. Zheng, L. Zheng, and Y. Yang · 2017
Closest in time.