Activity-Recognition Model for Violence Behavior Using LSTM
- Authors
- Kim, Svetlana; Nam, Hyejeong; Park, Hyunho; Lee, Yong-Tae; Yoon, Yongik
- Issue Date
- Jan-2021
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- Abnormal detection; Fusion sensing; LSTM; Smartphone; Smartwatch
- Citation
- Lecture Notes in Electrical Engineering, v.715, pp 529 - 535
- Pages
- 7
- Journal Title
- Lecture Notes in Electrical Engineering
- Volume
- 715
- Start Page
- 529
- End Page
- 535
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146849
- DOI
- 10.1007/978-981-15-9343-7_75
- ISSN
- 1876-1100
- 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.
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