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- W4383669954 abstract "Escorted by the advancement of technology, monitoring of real-time video data is playing an increasingly important role and massive impact on our lives. Therefore, correctly classifying the suspicious fall event detection during real-time video data monitoring is a highly appealing interest in the research area. In this paper, real-time events are considered for monitoring a person living in an indoor environment and performing their daily activity. Suspicious events are considered as detecting the falling of a person. Up to now, numerous techniques have been developed efficiently to classify suspicious fall event detection. An overview of numerous machine learning and deep learning techniques applied by several researchers for the detection of suspicious fall event detection in the monitoring of real-time video data has been considered. The paper also presents several affairs of machine learning and deep learning techniques that may assist focused researchers in working on monitoring video data." @default.
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- W4383669954 date "2023-01-01" @default.
- W4383669954 modified "2023-09-25" @default.
- W4383669954 title "Overview: Real-Time Video Monitoring for Suspicious Fall Event Detection" @default.
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- W4383669954 doi "https://doi.org/10.1007/978-981-99-0483-9_1" @default.
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