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Detail
Journal Article
Danger Detection Technology based on Multimodal and Multilog Data for Public Safety Services
- Authors
- Hyunho Park, Eunjung Kwon, Sungwon Byon, Won-Jae Shin, Eui-Suk Jung, Yong-Tae Lee
- Issue Date
- 2022-04
- Citation
- ETRI Journal, v.44, no.2, pp.300-312
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.
- KSP Keywords
- Abnormal situation, Attitude heading reference system, Data analysis, Detection technology, Global positioning system(GPS), Human activity, Over time, Public safety, Rapid detection, air pressure, danger detection
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
