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Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification

Cao, Min and Chen, Chen and Dou, Hao and Hu, Xiyuan and Peng, Silong and Kuijper, Arjan (2020):
Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification.
In: IEEE Transactions on Multimedia, ISSN 1520-9210,
DOI: 10.1109/TMM.2020.2994524,
[Article]

Abstract

Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a suboptimal ranking list. Instead, the pairwise similarity can be estimated more accurately along the geodesic path of the underlying data manifold by exploring the rich contextual information of the sample. In this paper, we propose a lightweight post-processing person re-identification method in which the pairwise measure is determined by the relationship between the sample and the counterpart's context in an unsupervised way. We translate the point-to-point comparison into the bilateral point-to-set comparison. The sample's context is composed of its neighbor samples with two different definition ways: the first order context and the second order context, which are used to compute the pairwise similarity in sequence, resulting in a progressive post-processing model. The experiments on four large-scale person re-identification benchmark datasets indicate that (1) the proposed method can consistently achieve higher accuracies by serving as a post-processing procedure after the content-based person re-identification methods, showing its state-of-the-art results, (2) the proposed lightweight method only needs about 6 milliseconds for optimizing the ranking results of one sample, showing its high-efficiency. Code is available at: https://github.com/123ci/PBCmodel.

Item Type: Article
Erschienen: 2020
Creators: Cao, Min and Chen, Chen and Dou, Hao and Hu, Xiyuan and Peng, Silong and Kuijper, Arjan
Title: Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification
Language: English
Abstract:

Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a suboptimal ranking list. Instead, the pairwise similarity can be estimated more accurately along the geodesic path of the underlying data manifold by exploring the rich contextual information of the sample. In this paper, we propose a lightweight post-processing person re-identification method in which the pairwise measure is determined by the relationship between the sample and the counterpart's context in an unsupervised way. We translate the point-to-point comparison into the bilateral point-to-set comparison. The sample's context is composed of its neighbor samples with two different definition ways: the first order context and the second order context, which are used to compute the pairwise similarity in sequence, resulting in a progressive post-processing model. The experiments on four large-scale person re-identification benchmark datasets indicate that (1) the proposed method can consistently achieve higher accuracies by serving as a post-processing procedure after the content-based person re-identification methods, showing its state-of-the-art results, (2) the proposed lightweight method only needs about 6 milliseconds for optimizing the ranking results of one sample, showing its high-efficiency. Code is available at: https://github.com/123ci/PBCmodel.

Journal or Publication Title: IEEE Transactions on Multimedia
Uncontrolled Keywords: Computer vision based tracking, Automatic identification, Context-awareness, Feature based modeling
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 25 May 2020 10:11
DOI: 10.1109/TMM.2020.2994524
Official URL: https://ieeexplore.ieee.org/document/9093179
Additional Information:

Early Access

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