  Monitoring and analyzing stereotypical behaviours is important for early
intervention and care taking in Autism Spectrum Disorder (ASD). This paper
focuses on automatically detecting stereotypical behaviours with computer
vision techniques. Off-the-shelf methods tackle this task by supervised
classification and activity recognition techniques. However, the unbounded
types of stereotypical behaviours and the difficulty in collecting video
recordings of ASD patients largely limit the feasibility of the existing
supervised detection methods. As a result, we tackle these challenges from a
new perspective, i.e. unsupervised video anomaly detection for stereotypical
behaviours detection. The models can be trained among unlabeled videos
containing only normal behaviours and unknown types of abnormal behaviours can
be detected during inference. Correspondingly, we propose a Dual Stream deep
model for Stereotypical Behaviours Detection, DS-SBD, based on the temporal
trajectory of human poses and the repetition patterns of human actions.
Extensive experiments are conducted to verify the effectiveness of our proposed
method and suggest that it serves as a potential benchmark for future research.
