| CPC G06T 7/0002 (2013.01) [G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/30168 (2013.01); G06T 2207/30232 (2013.01)] | 10 Claims |
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1. A video anomaly detection apparatus comprising:
a multi-frame predictor configured to predict a current frame and adjacent frames using a prediction model;
a multi-frame prediction error obtainer configured to obtain a multi-frame prediction error by obtaining a prediction error of the current frame and prediction errors of the adjacent frames that each prediction error is a difference between a predicted frame and a real frame and by adding the prediction error of the current frame and the prediction errors of the adjacent frames; and
an anomaly score evaluator configured to evaluate an anomaly score based on the obtained multi-frame prediction error of the current frame,
wherein the anomaly score evaluator is configured to
obtain the anomaly score for the current frame by using a cascade sliding window method for the obtained multi-frame prediction error,
perform, when moving the window, a movement process for the window such that the window moves evenly in both directions, by moving the window from bottom center to bottom left of the multi-frame prediction error by the size of the window,
each time the window moves, obtain a mean value for a patch of the multi-frame prediction error corresponding to the window, when the window reaches the bottom left, reposition the window at the bottom center, then move the window to bottom right of the multi-frame prediction error by the size of the window, and when the window reaches the bottom right, reposition the window at the bottom center again and then move the window upward by the size of the window,
then decrease the size of the window by a predetermined amount, repeatedly perform the movement process for the window, and in response that the window reaches top right of the multi-frame prediction error, sort mean values for patches of the multi-frame prediction error corresponding to the window, obtained each time the window moves, in ascending order, select a predetermined number of patches from the sorted ascending mean values, and average the selected patches to use as the anomaly score, and
during the movement process for the window, move a x-coordinate of the window to x=the size of the window(s) in response to the window being unable to reach left side of the multi-frame prediction error due to insufficient remaining space of the multi-frame prediction error, move the x-coordinate of the window to x=a width of the frame (w)−the size of the window(s) in response to the window being unable to reach right side of the multi-frame prediction error due to insufficient remaining space of the multi-frame prediction error, and move a y-coordinate of the window to y=the size of the window(s) in response to the window being unable to reach top side of the multi-frame prediction error due to insufficient remaining space of the multi-frame prediction error.
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