  We propose a semi-supervised model for detecting anomalies in videos
inspiredby the Video Pixel Network [van den Oord et al., 2016]. VPN is a
probabilisticgenerative model based on a deep neural network that estimates the
discrete jointdistribution of raw pixels in video frames. Our model extends the
Convolutional-LSTM video encoder part of the VPN with a novel convolutional
based attentionmechanism. We also modify the Pixel-CNN decoder part of the VPN
to a frameinpainting task where a partially masked version of the frame to
predict is given asinput. The frame reconstruction error is used as an anomaly
indicator. We test ourmodel on a modified version of the moving mnist dataset
[Srivastava et al., 2015]. Our model is shown to be effective in detecting
anomalies in videos. This approachcould be a component in applications
requiring visual common sense.
