NDLI: HPILN: a feature learning framework for cross-modality person re-identification

Content Provider	IET Digital Library
Author	Zhao, Yun Bo Lin, Jian Wu Xuan, Qi Xi, Xugang
Abstract	Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality variations caused by heterogeneous images in RGB and infrared, and the intra-modality variations caused by the heterogeneous human poses, camera position, light brightness etc. To meet these challenges, a novel feature learning framework, hard pentaplet and identity loss network (HPILN), is proposed. In the framework existing single-modality re-identification models are modified to fit for the cross-modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to increase the accuracy of the modified cross-modality re-identification models. Based on the benchmark of the SYSU-MM01 dataset, extensive experiments have been conducted, showing that the authors’ method outperforms all existing ones in terms of cumulative match characteristic curve and mean average precision.
Starting Page	2897
Ending Page	2904
Page Count	8
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 14, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0699
Journal	IET Image Processing
Publisher Date	2019-09-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Camera Camera Position Computer Vision And Image Processing Technique Cross-modality Person Cross-modality Scenario Cross-modality Variations Cumulative Match Characteristic Curve Feature Extraction Feature Learning Framework Heterogeneous Human Poses Heterogeneous Image HPILN Identity Loss Network Image Classification Image Colour Analysis Image Recognition Intra-modality Variations Knowledge Engineering Technique Learning in AI Mean Average Precision Modified Cross-modality Re-identification Model Pose Estimation RGB Single-modality Re-identification Model SYSU-MM01 Dataset Video Signal Processing Video Surveillance Video Surveillance System
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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