  People living with dementia often exhibit behavioural and psychological
symptoms of dementia that can put their and others' safety at risk. Existing
video surveillance systems in long-term care facilities can be used to monitor
such behaviours of risk to alert the staff to prevent potential injuries or
death in some cases. However, these behaviours of risk events are heterogeneous
and infrequent in comparison to normal events. Moreover, analyzing raw videos
can also raise privacy concerns. In this paper, we present two novel
privacy-protecting video-based anomaly detection approaches to detect
behaviours of risks in people with dementia. We either extracted body pose
information as skeletons or used semantic segmentation masks to replace
multiple humans in the scene with their semantic boundaries. Our work differs
from most existing approaches for video anomaly detection that focus on
appearance-based features, which can put the privacy of a person at risk and is
also susceptible to pixel-based noise, including illumination and viewing
direction. We used anonymized videos of normal activities to train customized
spatio-temporal convolutional autoencoders and identify behaviours of risk as
anomalies. We showed our results on a real-world study conducted in a dementia
care unit with patients with dementia, containing approximately 21 hours of
normal activities data for training and 9 hours of data containing normal and
behaviours of risk events for testing. We compared our approaches with the
original RGB videos and obtained a similar area under the receiver operating
characteristic curve performance of 0.807 for the skeleton-based approach and
0.823 for the segmentation mask-based approach.
