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- W4387145770 abstract "With the widespread deployment of surveillance equipment for social security purposes, video data analysis has become increasingly challenging. To address this problem, we study video anomaly detection for automatically identifying crime scenes. Our proposed method adopts an one-class semi-supervised strategy, assuming that the anomalous behaviors that need to be detected make up only a small fraction of the entire video frames. To facilitate effective detection, we adopt a two-step approach that utilizes a 3-dimensional convolutional neural network (3D CNN) and a Variational AutoEncoder-based Support Vector Data Description (VAE-SVDD). To further enhance the performance, we propose to add the Kullback-Leibler divergence loss to the anomaly score. Our method is evaluated on the Abnormal Behavior CCTV Video Dataset obtained from AI-Hub and compared to a state-of-the-art supervised approach. The experimental results demonstrate the effectiveness of our proposed method in detecting crime scenes." @default.
- W4387145770 created "2023-09-29" @default.
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- W4387145770 date "2023-01-01" @default.
- W4387145770 modified "2023-10-18" @default.
- W4387145770 title "Crime Scene Detection in Surveillance Videos Using Variational AutoEncoder-Based Support Vector Data Description" @default.
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- W4387145770 doi "https://doi.org/10.1007/978-3-031-42430-4_37" @default.
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