  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. We approach this problem by learning a generative model for regular
motion patterns, termed as regularity, using multiple sources with very limited
supervision. Specifically, we propose two methods that are built upon the
autoencoders for their ability to work with little to no supervision. We first
leverage the conventional handcrafted spatio-temporal local features and learn
a fully connected autoencoder on them. Second, we build a fully convolutional
feed-forward autoencoder to learn both the local features and the classifiers
as an end-to-end learning framework. Our model can capture the regularities
from multiple datasets. We evaluate our methods in both qualitative and
quantitative ways - showing the learned regularity of videos in various aspects
and demonstrating competitive performance on anomaly detection datasets as an
application.
