  We propose a solution to detect anomalous events in videos without the need
to train a model offline. Specifically, our solution is based on a
randomly-initialized multilayer perceptron that is optimized online to
reconstruct video frames, pixel-by-pixel, from their frequency information.
Based on the information shifts between adjacent frames, an incremental learner
is used to update parameters of the multilayer perceptron after observing each
frame, thus allowing to detect anomalous events along the video stream.
Traditional solutions that require no offline training are limited to operating
on videos with only a few abnormal frames. Our solution breaks this limit and
achieves strong performance on benchmark datasets.
