Training approach using the shallow model and hard triplet mining for person re‐identification

Author:

Choi Hyunguk1,Yow Kin Choong2,Jeon Moongu1

Affiliation:

1. The School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology123 Cheomdangwagi‐ro, Buk‐guGwangju61005Republic of Korea

2. Engineering and Applied ScienceUniversity of ReginaSaskatchewanCanada

Funder

Institute for Information and Communications Technology Promotion

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference54 articles.

1. Adaptive learning for target tracking and true linking discovering across multiple non‐overlapping cameras;Chen K.W.;IEEE Trans. Multimed.,2011

2. Wide area object tracking using multiple cameras with mixed configuration of over‐lapping and non‐overlapping FOV;Lin D.T.;Int. J. Multimed. Ubiquit. Eng.,2016

3. 2010 2010 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) San Francisco USA M. Farenzena L. Bazzani A. Perina Person reidentification by symmetry‐driven accumulation of local features 2360 2367

4. 2012 2012 Sixth Int. Conf. on Distributed Smart Cameras (ICDSC) Hong Kong M. Bredereck X. Jiang M. Körner Data association for multiobject tracking‐by‐detection in multi‐camera networks 1 6

5. Collaborative pedestrian tracking and data fusion with multiple cameras;Lin D.T.;IEEE Trans. Inf. Forensics Sec.,2011

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