Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring-Reference-Cited by-同舟云学术

Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring

Published:2023-03-01 Issue:5 Volume:10 Page:3735-3748
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Liu Shuai¹^ORCID,Wang Shuai¹,Liu Xinyu¹,Dai Jianhua¹,Muhammad Khan²^ORCID,Gandomi Amir H.³^ORCID,Ding Weiping⁴^ORCID,Hijji Mohammad⁵^ORCID,de Albuquerque Victor Hugo C.⁶^ORCID

Affiliation:

1. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, the College of Information Science and Engineering, Hunan Normal University, Changsha, China

2. Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Sungkyunkwan University, Seoul, Republic of Korea

3. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

4. School of Information Science and Technology, Nantong University, Nantong, China

5. Computer Science Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia

6. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Brazil

Funder

Natural Science Foundation of Hunan Province

Key Scientific Research Projects of Department of Education of Hunan Province

Key Research Project on Degree and Graduate Education Reform of Hunan Province

National Social Science Fund of China

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Natural Science Key Foundation of Jiangsu Education Department

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10049212/09676994.pdf?arnumber=9676994

Reference53 articles.

1. Object Tracking Using Local Multiple Features and a Posterior Probability Measure

2. Learning Background-Aware Correlation Filters for Visual Tracking

3. Object Tracking via Boosted Cascade of Simple Features and Coarse and Fine Structural Local Sparse Appearance Models

4. Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking

5. Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments

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