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Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness
| Content Provider | MDPI |
|---|---|
| Author | Pei, Shengyu Fan, Xiaoping |
| Copyright Year | 2022 |
| Description | Existing person re-recognition (Re-ID) methods usually suffer from poor generalization capability and over-fitting problems caused by insufficient training samples. We find that high-level attributes, semantic information, and part-based local information alignment are useful for person Re-ID networks. In this study, we propose a person re-recognition network with part-based attribute-enhanced features. The model includes a multi-task learning module, local information alignment module, and global information learning module. The ResNet based on non-local and instance batch normalization (IBN) learns more discriminative feature representations. The multi-task module, local module, and global module are used in parallel for feature extraction. To better prevent over-fitting, the local information alignment module transforms pedestrian attitude alignment into local information alignment to assist in attribute recognition. Extensive experiments are carried out on the Market-1501 and DukeMTMC-reID datasets, whose results demonstrate that the effectiveness of the method is superior to most current algorithms. |
| Starting Page | 120 |
| e-ISSN | 19994893 |
| DOI | 10.3390/a15040120 |
| Journal | Algorithms |
| Issue Number | 4 |
| Volume Number | 15 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-30 |
| Access Restriction | Open |
| Subject Keyword | Algorithms Artificial Intelligence Industrial Engineering Person Re-identification Attribute Awareness Non-local Multi-task |
| Content Type | Text |
| Resource Type | Article |
