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.
翻译:长期护理设施中现有的视频监视系统可用来监测这种风险行为,以便提醒工作人员注意某些情况下可能发生的伤亡。然而,这些风险事件的行为与正常事件相比是多种多样的,而且并不常见。此外,原始视频分析还可能引起隐私问题。在本文件中,我们介绍了两种新的基于隐私的保护视频异常检测方法,以发现患有痴呆症的人的风险行为。我们抽取的尸体要么将信息作为骨骼,要么使用语义分解面具取代现场的多人,以其语义界限取代现场的多人。我们的工作不同于大多数现有的侧重于外观特征的视频异常检测方法,这些特征可能使一个人的隐私面临风险,并且还容易受到基于像素的噪音,包括照明和观察方向。我们使用了两种基于隐私保护的视频的普通活动匿名视频,以培训自成定制的口腔-情绪自动分解器,并查明风险行为是异常的。我们用正常的视频分解方法展示了我们用于正常运行时间和正常运行风险的R小时活动的结果,我们用正常操作区域进行21小时的数据测试,并用正常操作系统进行21小时数据测试。