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Type: Conference paper
Title: Learning to rank in person re-identification with metric ensembles
Author: Paisitkriangkrai, S.
Shen, C.
Van Den Hengel, A.
Citation: Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol.07-12-June-2015, pp.1846-1855
Publisher: IEEE
Issue Date: 2015
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781467369640
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (7 Jun 2015 - 12 Jun 2015 : Boston, MA)
Statement of
Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel
Abstract: We propose an effective structured learning based ap- proach to the problem of person re-identification which out- performs the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the ba- sis of multiple low-level hand-crafted and high-level vi- sual features. We then formulate two optimization algo- rithms, which directly optimize evaluation measures com- monly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re- identification system which outperforms most existing al- gorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank- 1 recognition rates from 40% to 50% on the iLIDS bench- mark, 16% to 18% on the PRID2011 benchmark, 43% to 46% on the VIPeR benchmark, 34% to 53% on the CUHK01 benchmark and 21% to 62% on the CUHK03 benchmark.
Rights: Copyright © 2015, IEEE
DOI: 10.1109/CVPR.2015.7298794
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