  Recently, several techniques have been explored to detect unusual behaviour
in surveillance videos. Nevertheless, few studies leverage features from
pre-trained CNNs and none of then present a comparison of features generate by
different models. Motivated by this gap, we compare features extracted by four
state-of-the-art image classification networks as a way of describing patches
from security video frames. We carry out experiments on the Ped1 and Ped2
datasets and analyze the usage of different feature normalization techniques.
Our results indicate that choosing the appropriate normalization is crucial to
improve the anomaly detection performance when working with CNN features. Also,
in the Ped2 dataset our approach was able to obtain results comparable to the
ones of several state-of-the-art methods. Lastly, as our method only considers
the appearance of each frame, we believe that it can be combined with
approaches that focus on motion patterns to further improve performance.
