short-paper

Extensive Comparison of Visual Features for Person Re-identification

Authors:

Guanzhong Wang,

Jian SunAuthors Info & Claims

ICIMCS'16: Proceedings of the International Conference on Internet Multimedia Computing and Service

Pages 192 - 196

https://doi.org/10.1145/3007669.3007741

Published: 19 August 2016 Publication History

Abstract

Person re-identification is one of the most critical tasks in the field of computer vision and has widely applications for abnormal detection and object retrieval in video surveillance. In this paper, we give an extensive comparison for different kinds of visual features including hand-craft features and Convolutional Neural Networks (CNN) features. We run the experiments on three public dataset CASIA, Market1501 and CUHK03. Through A detail comparison and analysis on different features with different similarity measures, we find Colorhistogram and ScalableColor features are most robust to occlusion on CASIA, while GoogleNet and VG-GNet features have good robustness as well. For all single features, GoogleNet feature achieves the highest results on Market1501 and CUHK03. For feature fustion, GoogleNet feature with ColorStructure achieve the best result on Market1501 and GoogleNet feature wth Colorhistogram achieve the best result on CUHK03. For similarity measure, Cosine distance is evaluated to be the best one in our experiments.

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Cited By

Batool EGillani SNaz SBukhari MMaqsood MYeo SRho S(2023)POSNet: a hybrid deep learning model for efficient person re-identificationThe Journal of Supercomputing10.1007/s11227-023-05169-479:12(13090-13118)Online publication date: 22-Mar-2023
https://doi.org/10.1007/s11227-023-05169-4
Almasawa MElrefaei LMoria K(2019)A Survey on Deep Learning-Based Person Re-Identification SystemsIEEE Access10.1109/ACCESS.2019.29573367(175228-175247)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2957336

Index Terms

Extensive Comparison of Visual Features for Person Re-identification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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cover image ACM Other conferences

ICIMCS'16: Proceedings of the International Conference on Internet Multimedia Computing and Service

August 2016

360 pages

ISBN:9781450348508

DOI:10.1145/3007669

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Xidian University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 August 2016

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Short-paper
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Refereed limited

Funding Sources

National Natural Science Foundation of China
863 Program

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ICIMCS'16

ICIMCS'16: International Conference on Internet Multimedia Computing and Service

August 19 - 21, 2016

Xi'an, China

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ICIMCS'16 Paper Acceptance Rate 77 of 118 submissions, 65%;

Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

Batool EGillani SNaz SBukhari MMaqsood MYeo SRho S(2023)POSNet: a hybrid deep learning model for efficient person re-identificationThe Journal of Supercomputing10.1007/s11227-023-05169-479:12(13090-13118)Online publication date: 22-Mar-2023
https://doi.org/10.1007/s11227-023-05169-4
Almasawa MElrefaei LMoria K(2019)A Survey on Deep Learning-Based Person Re-Identification SystemsIEEE Access10.1109/ACCESS.2019.29573367(175228-175247)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2957336

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