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.

本文采用计算机视觉技术进行自动检测孤独症谱系障碍(Autism Spectrum Disorder, ASD)患者的刻板行为, 并从非监督学习的角度解决监督学习方法难以应用于多种类型的刻板行为且数据难以获取的问题. 通过提出一种基于DS-SBD模型的双流深度模型，该模型可以有效地检测ASD患者中的刻板行为, 从而为未来的研究提供潜在的基准。

自闭症典型行为的无监督视频异常检测