Illumination-Adaptive Person Re-Identification

Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is common, and person images are often captured under different illumination conditions at different times across a day. In...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2020-12, Vol.22 (12), p.3064-3074
Main Authors: Zeng, Zelong, Wang, Zhixiang, Wang, Zheng, Zheng, Yinqiang, Chuang, Yung-Yu, Satoh, Shin'ichi
Format: Article
Language:English
Subjects:
Cameras
Datasets
feature disentanglement
Feature extraction
Generators
Illumination
illumination-adaptive
Lighting
Person re-identification
Testing
Training data
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Summary:Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is common, and person images are often captured under different illumination conditions at different times across a day. In this situation, the performances of existing ReID models often degrade dramatically. This paper addresses the ReID problem with illumination variations and names it as Illumination-Adaptive Person Re-identification (IA-ReID) . We propose an Illumination-Identity Disentanglement (IID) network to dispel different scales of illuminations away while preserving individuals' identity information. To demonstrate the illumination issue and to evaluate our model, we construct two large-scale simulated datasets with a wide range of illumination variations. Experimental results on the simulated datasets and real-world images demonstrate the effectiveness of the proposed framework.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2020.2969782