Title:
DEEP LEARNING-BASED ABNORMAL BEHAVIOR DETECTION SYSTEM THROUGH DE-IDENTIFICATION DATA ANALYSIS
Document Type and Number:
WIPO Patent Application WO/2023/022429
Kind Code:
A1
Abstract:
The present invention relates to a deep learning-based abnormal behavior detection system which detects abnormal behavior such as installation of a hidden camera in a predetermined area such as a restroom by using de-identification data. The present invention comprises: a de-identification image information generation unit which detects a subject's behavior in a predetermined area and generates image information data in which the subject's personal information is de-identified; and an abnormal behavior discrimination unit which classifies the de-identified image information data into normal behavior data or abnormal behavior data using a learning model for abnormal behavior.
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Inventors:
HAN SOO YEON (KR)
Application Number:
PCT/KR2022/011935
Publication Date:
February 23, 2023
Filing Date:
August 10, 2022
Export Citation:
Assignee:
UNIUNI CORP (KR)
International Classes:
G08B13/196; G08B5/22; G08B25/10; H04N7/18
Foreign References:
| KR20200059643A | 2020-05-29 | |||
| KR20200088236A | 2020-07-22 | |||
| KR20210062256A | 2021-05-31 | |||
| KR20190139808A | 2019-12-18 | |||
| US20120134532A1 | 2012-05-31 |
Attorney, Agent or Firm:
HAEDAM IP GROUP (KR)
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