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- W4301431348 abstract "Abnormal event detection is one of the most huge objectives in analysis sector and viable applications of motion picture intelligence. To improve public safety, reconnaissance cameras are progressively being used in broad daylight spaces like streets, junction, banks, and shopping centers. One most critical limit in video structure is perceiving uncommon activity, for instance, car accidents, bad behaviors, or other illegal exercises. Generally, odd events are only sometimes happened when diverged from standard activities. By and large, atypical occasions seldom happen when contrasted with typical exercises. The target of an irregularity revelation structure is to advantageous sign a development that veers off from customary examples and to recognize the time window of the inconsistency happening in the framework. Once an abnormal activity has been identified, classification techniques are utilized to segregate it into distinct activities. This paper provides an overview of anomaly detection, with an emphasis on the approach of detecting odd actions in banking operations. Banking operation includes daily, weekly, and a periodic activities and transactions performed by or affecting variety of stakeholders such as employees, clients, and external entities. Events may open out over time, and early discovery can considerably mitigate potential negative consequences, and in some cases actively prevent the same. Inconsistency recognition dependent on time series is utilized to recognize individuals during badly designed occasions. In this work AI based oddity recognition strategy carried out to perceive the typical and unusual occasions." @default.
- W4301431348 created "2022-10-05" @default.
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- W4301431348 date "2022-10-06" @default.
- W4301431348 modified "2023-09-25" @default.
- W4301431348 title "Anomaly Detection for Bank Security Against Theft—A Survey" @default.
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- W4301431348 doi "https://doi.org/10.1007/978-981-19-3575-6_31" @default.
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