OCCLUSION-INVARIANT REPRESENTATION ALIGNMENT FOR ENTITY RE-IDENTIFICATION

Zhanghao Jiang, Ke Xu, Heshan Du, Huan Jin, Zheng Lu, Qian Zhang

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

Abstract

Entity re-identification is the foundation of tracking- and matching-based computer vision tasks, which are widely employed in a variety of applications. However, when trained exclusively on clear images, the models capacity to generalize is significantly affected by the presence of occlusion at referencing time, whereas data argumentation-based approaches are costly to construct without guaranteeing a test-time improvement. To tackle this problem, we propose a domain adaptation framework based on learning representations that generates occlusion-invariant feature representations by aligning the clean image embedding distribution with the occluded one, using a disparity discrepancy metric derived from the siamese network architecture. Without the need for additional processing modules during the inference stage or an expensive occlusion-augmentation-enlarged dataset during the training stage, we could obtain occlusion invariant embeddings that are free of the impact of occluders. Extensive experimental results for two tasks across three datasets indicate the proposed method's robustness and effectiveness to a variety of occlusions at all levels.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PublisherIEEE Computer Society
Pages3266-3270
Number of pages5
ISBN (Electronic)9781665496209
DOIs
Publication statusPublished - 2022
Event29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
Duration: 16 Oct 202219 Oct 2022

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
Country/TerritoryFrance
CityBordeaux
Period16/10/2219/10/22

Keywords

  • Domain adaptation
  • Occlusion invariant
  • Re-identification
  • Siamese network

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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