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- W4293921342 abstract "As surveillance technology is continuously improving, an ever-increasing number of cameras are being deployed everywhere. Relying on manual detection of anomalies through cameras may be unreliable and untimely. Therefore, the application of deep learning in video anomaly detection is being extensively studied. Anomaly Detection (AD) refers to identifying events that deviate from the desired actions. This article discusses representative unsupervised and weakly-supervised learning methods applied to various data types. In these machine learning methods, Generative Adversarial Network, Auto Encoder, Recurrent Neural Network, etc. are broadly adopted for AD. Some renowned and new datasets are reviewed. Furthermore, we also proposed several future directions of research in video anomaly detection." @default.
- W4293921342 created "2022-09-01" @default.
- W4293921342 creator A5089042771 @default.
- W4293921342 date "2022-08-26" @default.
- W4293921342 modified "2023-10-18" @default.
- W4293921342 title "Weakly-Supervised and Unsupervised Video Anomaly Detection" @default.
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- W4293921342 doi "https://doi.org/10.54097/hset.v12i.1444" @default.
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