Learning Bias-Free Representation for Large-Scale Person Re-Identification
Person re-identification (re-ID) aims to match the same person across disjointed cameras. In practice, misalignment is one of the key problems that limits the re-ID accuracy. There are many causes, such as detection errors from automatic detectors, human pose changes and relative movement between pe...
Main Authors: | Jiaming Xu, En Zhu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8813095/ |
Similar Items
-
MSBA: Multiple Scales, Branches and Attention Network With Bag of Tricks for Person Re-Identification
by: Hanlin Tan, et al.
Published: (2020-01-01) -
Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification
by: Yuting Liu, et al.
Published: (2019-05-01) -
Learning Multi-Scale Features and Batch-Normalized Global Features for Person Re-Identification
by: Zongjing Cao, et al.
Published: (2020-01-01) -
Person Re-Identification Net of Spindle Net Fusing Facial Feature
Published: (2019-10-01) -
Integration Convolutional Neural Network for Person Re-Identification in Camera Networks
by: Zhong Zhang, et al.
Published: (2018-01-01)