Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Improved Statistical Interference Model for Person Re-identification

Authors
Linxuan Li
Corresponding Author
Linxuan Li
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.12How to use a DOI?
Keywords
Person re-identification, statistical interference, metric learning.
Abstract
Person Re-identification problem is an important and challenging task in computer vision task. Due to the drastic appearance variation caused by misalignment and illumination changing, traditional metric models are failed in similarity measure of pedestrian images. In this paper, a novel metric learning based method is proposed. It establishes a probability inference model based on the probability models of positive pairs and negative pairs. And a balance parameter is proposed in the metric model to deal with the imbalance problem of samples. Finally, experiments are conducted on the VIPeR dataset compared with some metric learning based model. And the test results verified the effectiveness of the proposed model.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.12How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Linxuan Li
PY  - 2019/04
DA  - 2019/04
TI  - Improved Statistical Interference Model for Person  Re-identification
BT  - 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.12
DO  - https://doi.org/10.2991/icmeit-19.2019.12
ID  - Li2019/04
ER  -