Learning Dual-Level Deep Representation for Thermal Infrared Tracking-Reference-Cited by-同舟云学术

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Learning Dual-Level Deep Representation for Thermal Infrared Tracking

Published:2023 Issue: Volume:25 Page:1269-1281
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Liu Qiao¹^ORCID,Yuan Di²^ORCID,Fan Nana³,Gao Peng⁴,Li Xin³^ORCID,He Zhenyu³^ORCID

Affiliation:

1. National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, China

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

3. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

4. School of Cyber Science and Engineering, Qufu Normal University, Shandong, China

Funder

National Natural Science Foundation of China

Shenzhen Research Council

Special Research Project on COVID-19 Prevention and Control of Guangdong Province

Foundation Project of Chongqing Normal University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6046/10016790/09672761.pdf?arnumber=9672761

Reference75 articles.

1. Deep convolutional neural networks for thermal infrared object tracking

2. ImageNet Large Scale Visual Recognition Challenge

3. Evaluation of Feature Channels for Correlation-Filter-Based Visual Object Tracking in Infrared Spectrum

4. GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

5. Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking

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