Ensemble Learning-Based Person Re-identification with Multiple Feature Representations

Joint Authors

Yang, Yun
Liu, Xiaofang
Ye, Qiongwei
Tao, Dapeng

Source

Complexity

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-09-04

Country of Publication

Egypt

No. of Pages

12

Main Subjects

Philosophy

Abstract EN

As an important application in video surveillance, person reidentification enables automatic tracking of a pedestrian through different disjointed camera views.

It essentially focuses on extracting or learning feature representations followed by a matching model using a distance metric.

In fact, person reidentification is a difficult task because, first, no universal feature representation can perfectly identify the amount of pedestrians in the gallery obtained by a multicamera system.

Although different features can be fused into a composite representation, the fusion still does not fully explore the difference, complementarity, and importance between different features.

Second, a matching model always has a limited amount of training samples to learn a distance metric for matching probe images against a gallery, which certainly results in an unstable learning process and poor matching result.

In this paper, we address the issues of person reidentification by the ensemble theory, which explores the importance of different feature representations, and reconcile several matching models on different feature representations to an optimal one via our proposed weighting scheme.

We have carried out the simulation on two well-recognized person reidentification benchmark datasets: VIPeR and ETHZ.

The experimental results demonstrate that our approach achieves state-of-the-art performance.

American Psychological Association (APA)

Yang, Yun& Liu, Xiaofang& Ye, Qiongwei& Tao, Dapeng. 2018. Ensemble Learning-Based Person Re-identification with Multiple Feature Representations. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1134887

Modern Language Association (MLA)

Yang, Yun…[et al.]. Ensemble Learning-Based Person Re-identification with Multiple Feature Representations. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1134887

American Medical Association (AMA)

Yang, Yun& Liu, Xiaofang& Ye, Qiongwei& Tao, Dapeng. Ensemble Learning-Based Person Re-identification with Multiple Feature Representations. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1134887

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1134887