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Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training.
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene.
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Next, features are extracted from each frame using a convolutional neural network (CNN) that is trained to classify between normal and abnormal frames.