Semantic consistent feature construction and multi-granularity feature learning for visible-infrared person re-identification

In the real-world 24/7 surveillance systems, the images collected during the day and night are visible light images and infrared images, respectively. Infrared images lack color and texture information. In this case, it is more practical to use cross-modality person re-identification (re-ID) to proc...

Full description

Saved in:
Bibliographic Details
Published in:The Visual computer 2024-04, Vol.40 (4), p.2363-2379
Main Authors: Wang, Yiming, Xu, Kaixiong, Chai, Yi, Jiang, Yutao, Qi, Guanqiu
Format: Article
Language:English
Subjects:
Artificial Intelligence
Body parts
Cameras
Channels
Computer Graphics
Computer Science
Feature extraction
Feature maps
Image Processing and Computer Vision
Infrared imagery
Misalignment
Modules
Original Article
Pedestrians
Semantics
Surveillance
Surveillance systems
Teaching methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the real-world 24/7 surveillance systems, the images collected during the day and night are visible light images and infrared images, respectively. Infrared images lack color and texture information. In this case, it is more practical to use cross-modality person re-identification (re-ID) to process visible-infrared images. In fact, the cross-modality semantic alignment and specific discriminative feature extraction of different modalities are important for the improvement of modal performance. Therefore, a Semantic Consistent Feature Construction and Multi-granularity Feature learning (SCC–MGL) method is proposed for visible-infrared person re-ID in this paper. The SCC–MGL consists of a Semantic Consistent Feature Construction (SCC) module and a Multi-Granularity Information Enhancement (MGIE) module. In SCC, the features of different modalities are guided by analyzing the relation between feature maps channels and pedestrian’s body parts to form consistent semantic information on the corresponding channels, which reduces the impact caused by the misalignment of semantic information. In MGIE, a local modality difference elimination strategy is proposed to remove the modality difference. Meanwhile, the local feature discrimination is improved by reasonably constraining multi-granularity features. The effectiveness and superiority of proposed method are validated by experimental results from SYSU-MM01 and RegDB datasets.
ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-023-02923-w