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Tsinghua Science and Technology 2018, 23(2): 145-156 https://doi.org/10.26599/TST.2018.9010060
Open Access | Issue | Published: 02 April 2018

Improved Bag-of-Words Model for Person Re-identification

Show Author's Information Lu Tian,  Shengjin Wang( )
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Keywords:
unsupervised learning, person re-identification, bag-of-words, feature fusion
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Tian L, Wang S. Improved Bag-of-Words Model for Person Re-identification. Tsinghua Science and Technology, 2018, 23(2): 145-156. https://doi.org/10.26599/TST.2018.9010060
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Abstract Full text About this article

Person re-identification (person re-id) aims to match observations on pedestrians from different cameras. It is a challenging task in real word surveillance systems and draws extensive attention from the community. Most existing methods are based on supervised learning which requires a large number of labeled data. In this paper, we develop a robust unsupervised learning approach for person re-id. We propose an improved Bag-of-Words (iBoW) model to describe and match pedestrians under different camera views. The proposed descriptor does not require any re-id labels, and is robust against pedestrian variations. Experiments show the proposed iBoW descriptor outperforms other unsupervised methods. By combination with efficient metric learning algorithms, we obtained competitive accuracy compared to existing state-of-the-art methods on person re-identification benchmarks, including VIPeR, PRID450S, and Market1501.


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About this article

Improved Bag-of-Words Model for Person Re-identification

Show Author's information Lu Tian, Shengjin Wang( )
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

Abstract

Person re-identification (person re-id) aims to match observations on pedestrians from different cameras. It is a challenging task in real word surveillance systems and draws extensive attention from the community. Most existing methods are based on supervised learning which requires a large number of labeled data. In this paper, we develop a robust unsupervised learning approach for person re-id. We propose an improved Bag-of-Words (iBoW) model to describe and match pedestrians under different camera views. The proposed descriptor does not require any re-id labels, and is robust against pedestrian variations. Experiments show the proposed iBoW descriptor outperforms other unsupervised methods. By combination with efficient metric learning algorithms, we obtained competitive accuracy compared to existing state-of-the-art methods on person re-identification benchmarks, including VIPeR, PRID450S, and Market1501.

Keywords: unsupervised learning, person re-identification, bag-of-words, feature fusion

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Received: 04 December 2016
Revised: 22 January 2017
Accepted: 25 January 2017
Published: 02 April 2018
Issue date: April 2018

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© The author(s) 2018

Acknowledgements

The work was supported by the National Natural Science Foundation of China (No. 61071135) and the National Science and Technology Support Program (No. 2013BAK02B04).

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