Person Re-identification Across Multiple Non-overlapping Cameras by Grouping Similarity Comparison Model

TAN Feigang; LIU Weiming; HUANG Ling; ZHAI Cong; SHI Wei; LI Yanshan

doi:10.1049/cje.2016.08.007

Volume 26 Issue 5

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TAN Feigang, LIU Weiming, HUANG Ling, et al., “Person Re-identification Across Multiple Non-overlapping Cameras by Grouping Similarity Comparison Model,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 905-911, 2017, doi: 10.1049/cje.2016.08.007

Citation:

TAN Feigang, LIU Weiming, HUANG Ling, et al., “Person Re-identification Across Multiple Non-overlapping Cameras by Grouping Similarity Comparison Model,” Chinese Journal of Electronics, vol. 26, no. 5, pp. 905-911, 2017, doi: 10.1049/cje.2016.08.007

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Person Re-identification Across Multiple Non-overlapping Cameras by Grouping Similarity Comparison Model

doi: 10.1049/cje.2016.08.007

1.
South China University of Technology, Guangzhou 510641, China;
2.
Shenzhen Institute of Information technology, Shenzhen 518172, China;
3.
ATR Key Laboratory of Defense Technology, Shenzhen University, Shenzhen 518060, China

Funds: This work is supported by the National Natural Science Foundation of China (No.6140128, No.51408237), Foundation for Distinguished Young Talents in Higher Education of Guangdong (No.2014KQNCX132), National Defense Preliminary Research Project (No.9140C800502150C80341), the scientific research fund of the Hunan provincial Education Department of China (No.13B132), Natural Science Foundation of SZU (No.201415), and Natural Science Foundation of Hubei Province (No.2015CFA025).

More Information

Corresponding author: LI Yanshan (corresponding author) was born in 1979, he received the Ph.D. degree in South China University of Technology. He is currently an associate professor at College of Information Engineering, Shenzhen University. His research interests include computer vision, machine learning and image analysis. (Email:lys@szu.edu.cn)
Received Date: 2015-09-06
Rev Recd Date: 2015-11-06
Publish Date: 2017-09-10

Abstract

Abstract

We propose a novel algorithm to solve the problem of person re-identification across multiple nonoverlapping cameras by grouping similarity comparison model. We use an image sequence instead of an image as a probe, and divide image sequence into groups by the method of systematic sampling. Then we design the rule which uses full-connection in a group and non-connection between groups to calculate similarities between images. We take the similarities as features, and train an AdaBoost classifier to match the persons across disjoint views. To enhance Euclidean distance discriminative ability, we propose a novel measure of similarity which is called Significant difference distance (SDD). Extensive experiments are conducted on two public datasets. Our proposed person re-identification method can achieve better performance compared with the state-of-the-art.
- Person re-identification,
- Significant difference distance (SDD),
- Grouping similarity comparison model,
- Across camera tracking,
- Non-overlapping camera views

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References(18)

References

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