Illumination-Adaptive Person Re-Identification
Journal
IEEE Transactions on Multimedia
Journal Volume
22
Journal Issue
12
Pages
3064-3074
Date Issued
2020
Author(s)
Abstract
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. ? 1999-2012 IEEE.
Subjects
Multimedia systems; Signal processing; Identity information; Illumination conditions; Illumination variation; Person re identifications; Real-world image; Simulated datasets; Large dataset
SDGs
Type
journal article