RT3C: Real-Time Crowd Counting in Multi-Scene Video Streams via Cloud-Edge-Device Collaboration-Reference-Cited by-同舟云学术

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RT3C: Real-Time Crowd Counting in Multi-Scene Video Streams via Cloud-Edge-Device Collaboration

Published:2024-07 Issue:4 Volume:17 Page:1739-1752
ISSN:1939-1374
Container-title:IEEE Transactions on Services Computing
language:
Short-container-title:IEEE Trans. Serv. Comput.

Author:

Wang Rui¹^ORCID,Hao Yixue¹^ORCID,Miao Yiming²^ORCID,Hu Long¹^ORCID,Chen Min³^ORCID

Affiliation:

1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

2. Shenzhen Institute of Artiffcial Intelligence and Robotics for Society (AIRS), School of Data Science, The Chinese University of Hong Kong, Shenzhen, Guangdong, China

3. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Shenzhen Science and Technology Program

Shenzhen Institute of Artificial Intelligence and Robotics for Society

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4629386/10630587/10472084.pdf?arnumber=10472084

Reference55 articles.

1. Real-Time Crowd Monitoring Using Seamless Indoor-Outdoor Localization

2. Enforcing Privacy Preservation on Edge Cameras using Lightweight Video Frame Scrambling

3. Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service

4. Combining Resource-Aware Recommendation and Caching in the Era of MEC for Improving the Experience of Video Streaming Users

5. Reducto

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