The existing methods for video anomaly detection mostly utilize videos
containing identifiable facial and appearance-based features. The use of videos
with identifiable faces raises privacy concerns, especially when used in a
hospital or community-based setting. Appearance-based features can also be
sensitive to pixel-based noise, straining the anomaly detection methods to
model the changes in the background and making it difficult to focus on the
actions of humans in the foreground. Structural information in the form of
skeletons describing the human motion in the videos is privacy-protecting and
can overcome some of the problems posed by appearance-based features. In this
paper, we present a survey of privacy-protecting deep learning anomaly
detection methods using skeletons extracted from videos. We present a novel
taxonomy of algorithms based on the various learning approaches. We conclude
that skeleton-based approaches for anomaly detection can be a plausible
privacy-protecting alternative for video anomaly detection. Lastly, we identify
major open research questions and provide guidelines to address them.Comment: This work has been accepted by IEEE Transactions on Emerging Topics
in Computational Intelligenc