research-article

Person Re-Identification Using Multi-region Triplet Convolutional Network

Author:

Bogdan KwolekAuthors Info & Claims

ICDSC 2017: Proceedings of the 11th International Conference on Distributed Smart Cameras

Pages 82 - 87

https://doi.org/10.1145/3131885.3131917

Published: 05 September 2017 Publication History

Abstract

Person re-identification is a difficult task due to variations of person pose, scale changes, different illumination, occlusions, to name a few important factors usually diminishing identification performance across different views. In this work, we train a siamese and triplet convolutional neural networks and show that they can achieve promising recognition ratios. In order to cope with spatial transformations and scale changes across multi-view images we employ deformable convolutions in a triplet convolutional neural network. We propose an unified neural network architecture consisting of three triplet convolutional neural networks to jointly learn both the local body-parts features and full-body descriptors. We demonstrate experimentally that it achieves comparable results with results achieved by state-of-the-arts methods.

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Cited By

Kim YChoi SLee HKim TKim C(2020)RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation2020 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV45572.2020.9093294(2046-2054)Online publication date: Mar-2020
https://doi.org/10.1109/WACV45572.2020.9093294
Dong HLu PLiu CJi YGong S(2018)Learning Multiple Kernel Metrics for Iterative Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/323492914:3(1-24)Online publication date: 9-Aug-2018
https://dl.acm.org/doi/10.1145/3234929

Index Terms

Person Re-Identification Using Multi-region Triplet Convolutional Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Matching
      2. Computer vision tasks
        Biometrics
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Other conferences

ICDSC 2017: Proceedings of the 11th International Conference on Distributed Smart Cameras

September 2017

221 pages

ISBN:9781450354875

DOI:10.1145/3131885

Copyright © 2017 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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Stanford University: Stanford University

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New York, NY, United States

Publication History

Published: 05 September 2017

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ICDSC 2017

ICDSC 2017: International Conference on Distributed Smart Cameras

September 5 - 7, 2017

CA, Stanford, USA

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Cited By

Kim YChoi SLee HKim TKim C(2020)RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation2020 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV45572.2020.9093294(2046-2054)Online publication date: Mar-2020
https://doi.org/10.1109/WACV45572.2020.9093294
Dong HLu PLiu CJi YGong S(2018)Learning Multiple Kernel Metrics for Iterative Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/323492914:3(1-24)Online publication date: 9-Aug-2018
https://dl.acm.org/doi/10.1145/3234929

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