Privacy-preserving Online Human Behaviour Anomaly Detection Based on Body Movements and Objects Positions

Human behaviour anomaly detection is crucial for modern artifi-cial intelligence systems. However, privacy protection plays a great role in the realization. In this paper, an online privacy-preserving anomaly detector is presented. The proposed method is able to discriminate on human subject body movements, postures and interactions with the surrounding objects, preserving subject privacy in all the tuning, training and testing stages. ActionXPose, Single Shot MultiBox Detector and Support Vector Machine are exploited for the proposed semi-supervised anomaly detector. The method successfully detects abnormal human behaviours, including unexpected body movements and misplaced objects. A new dataset ISLD-A is also proposed
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, providing suitable benchmark for performance evaluation
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.