  Abnormal activity recognition requires detection of occurrence of anomalous
events that suffer from a severe imbalance in data. In a video, normal is used
to describe activities that conform to usual events while the irregular events
which do not conform to the normal are referred to as abnormal. It is far more
common to observe normal data than to obtain abnormal data in visual
surveillance. In this paper, we propose an approach where we can obtain
abnormal data by transforming normal data. This is a challenging task that is
solved through a multi-stage pipeline approach. We utilize a number of
techniques from unsupervised segmentation in order to synthesize new samples of
data that are transformed from an existing set of normal examples. Further,
this synthesis approach has useful applications as a data augmentation
technique. An incrementally trained Bayesian convolutional neural network (CNN)
is used to carefully select the set of abnormal samples that can be added.
Finally through this synthesis approach we obtain a comparable set of abnormal
samples that can be used for training the CNN for the classification of normal
vs abnormal samples. We show that this method generalizes to multiple settings
by evaluating it on two real world datasets and achieves improved performance
over other probabilistic techniques that have been used in the past for this
task.
