STFE: A Comprehensive Video-Based Person Re-Identification Network Based on Spatio-Temporal Feature Enhancement-Reference-Cited by-同舟云学术

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STFE: A Comprehensive Video-Based Person Re-Identification Network Based on Spatio-Temporal Feature Enhancement

Published:2024 Issue: Volume:26 Page:7237-7249
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Yang Xi¹^ORCID,Wang Xian²^ORCID,Liu Liangchen¹^ORCID,Wang Nannan¹^ORCID,Gao Xinbo³^ORCID

Affiliation:

1. State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China

2. Hangzhou Institute of Technology, Xidian University, Hangzhou, China

3. Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China

Funder

National Natural Science Foundation of China

Shaanxi Outstanding Youth Science Fund Project

Technology Innovation Leading Program of Shaanxi

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6046/10384483/10420506.pdf?arnumber=10420506

Reference74 articles.

1. Recurrent Convolutional Network for Video-Based Person Re-identification

2. A Two Stream Siamese Convolutional Neural Network for Person Re-identification

3. Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks

4. Dense 3D-Convolutional Neural Network for Person Re-Identification in Videos

5. Two-stream convolutional networks for action recognition in videos;Simonyan,2014

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1. Fast high quality highlight removal using a simplified matrix iteration method;Signal Processing;2025-01

2. A Spatio-temporal Graph Transformer driven model for recognizing fine-grained data human activity;Alexandria Engineering Journal;2024-10

3. Mini-3DCvT: a lightweight lip-reading method based on 3D convolution visual transformer;The Visual Computer;2024-06-11

4. Deep video-based person re-identification (Deep Vid-ReID): comprehensive survey;EURASIP Journal on Advances in Signal Processing;2024-05-15

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