Efficient Deep Learning Models for Privacy-Preserving People Counting on Low-Resolution Infrared Arrays-Reference-Cited by-同舟云学术

Efficient Deep Learning Models for Privacy-Preserving People Counting on Low-Resolution Infrared Arrays

Published:2023-08-01 Issue:15 Volume:10 Page:13895-13907
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Xie Chen¹^ORCID,Daghero Francesco¹^ORCID,Chen Yukai²^ORCID,Castellano Marco³,Gandolfi Luca³^ORCID,Calimera Andrea¹^ORCID,Macii Enrico⁴^ORCID,Poncino Massimo¹^ORCID,Jahier Pagliari Daniele¹^ORCID

Affiliation:

1. Department of Control and Computer Engineering, Politecnico di Torino, Turin, Italy

2. IMEC, Leuven, Belgium

3. AMS R&D Department, STMicroelectronics S.r.l, Cornaredo, Italy

4. Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, Italy

Funder

ECSEL Joint Undertaking

European Union’s Horizon 2020 Research and Innovation Programme and France, Belgium, Czech Republic, Germany, Italy, Sweden, Switzerland, Turkey

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/ielx7/6488907/10194321/10089174.pdf?arnumber=10089174

Reference58 articles.

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3. A Comparative Study of Vision Based Human Detection Techniques in People Counting Applications

4. X-CUBE-AI, AI expansion pack for STM32CubeMX;STMicroelectronics,2023

5. New research on People Counting and Human Detection

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