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Activity-Recognition Model for Violence Behavior Using LSTM
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Svetlana | - |
| dc.contributor.author | Nam, Hyejeong | - |
| dc.contributor.author | Park, Hyunho | - |
| dc.contributor.author | Lee, Yong-Tae | - |
| dc.contributor.author | Yoon, Yongik | - |
| dc.date.accessioned | 2022-04-19T09:29:33Z | - |
| dc.date.available | 2022-04-19T09:29:33Z | - |
| dc.date.issued | 2021-01 | - |
| dc.identifier.issn | 1876-1100 | - |
| dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146849 | - |
| dc.description.abstract | Among many dangerous situations, the number of cases of violence has been growing recently. However, there is currently no research to recognize conditions such as assault. Therefore, this paper presents a VR (Violence-Recognition) model for recognition activity using LSTM. The VR model develops algorithms that can detect dangerous situations through processing and analysis of sensing data. Also, to improve accuracy by using the FFT algorithm for processing digital signals in combination with LSTM. ? 2021, Springer Nature Singapore Pte Ltd. | - |
| dc.format.extent | 7 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
| dc.title | Activity-Recognition Model for Violence Behavior Using LSTM | - |
| dc.type | Article | - |
| dc.publisher.location | 독일 | - |
| dc.identifier.doi | 10.1007/978-981-15-9343-7_75 | - |
| dc.identifier.scopusid | 2-s2.0-85101522995 | - |
| dc.identifier.bibliographicCitation | Lecture Notes in Electrical Engineering, v.715, pp 529 - 535 | - |
| dc.citation.title | Lecture Notes in Electrical Engineering | - |
| dc.citation.volume | 715 | - |
| dc.citation.startPage | 529 | - |
| dc.citation.endPage | 535 | - |
| dc.type.docType | Conference Paper | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.subject.keywordPlus | Ubiquitous computing | - |
| dc.subject.keywordPlus | Activity recognition | - |
| dc.subject.keywordPlus | Dangerous situations | - |
| dc.subject.keywordPlus | Digital signals | - |
| dc.subject.keywordPlus | FFT algorithm | - |
| dc.subject.keywordPlus | Sensing data | - |
| dc.subject.keywordPlus | Long short-term memory | - |
| dc.subject.keywordAuthor | Abnormal detection | - |
| dc.subject.keywordAuthor | Fusion sensing | - |
| dc.subject.keywordAuthor | LSTM | - |
| dc.subject.keywordAuthor | Smartphone | - |
| dc.subject.keywordAuthor | Smartwatch | - |
| dc.identifier.url | https://link.springer.com/chapter/10.1007%2F978-981-15-9343-7_75 | - |
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