What makes you look like you: Learning an inherent feature representation for person re-identification

Wen Li Wei, Jen Chun Lin, Yen Yu Lin, Hong Yuan Mark Liao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this work, we address person re-identification (ReID) by learning an inherent feature representation (inherent code) that is unique to each individual. This task is difficult because the appearance of a person may vary dramatically due to diverse factors, such as illuminations, viewpoints, and human pose changes. To tackle this issue, we propose new learning objectives to learn the inherent code for each person based on deep learning. Specifically, the proposed deep-net model is trained by jointly optimizing the multiple objectives that pulls the instances of the same person closer while pushing the instances belonging to different persons far from each other. Owing to such complementary designs, the deep-net model yields a robust code for each individual and hence better solve person ReID. Promising experimental results demonstrate the robustness and effectiveness of our proposed method.

Original languageEnglish
Title of host publication2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728109909
DOIs
StatePublished - Sep 2019
Event16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019 - Taipei, Taiwan
Duration: 18 Sep 201921 Sep 2019

Publication series

Name2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019

Conference

Conference16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
CountryTaiwan
CityTaipei
Period18/09/1921/09/19

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