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.

翻译：监测和分析刻板行为对于自闭症谱系障碍（ASD）的早期干预和护理至关重要。本文聚焦于利用计算机视觉技术自动检测刻板行为。现有方法通常通过监督式分类和活动识别技术来处理此任务。然而，刻板行为类型的无限性以及收集ASD患者视频素材的困难性，极大限制了现有监督检测方法的可行性。为此，我们从全新视角应对这些挑战——即采用无监督视频异常检测方法识别刻板行为。该模型可在仅包含正常行为的未标注视频上训练，并在推理阶段检测未知类型的异常行为。基于此，我们提出一种用于刻板行为检测的双流深度模型（DS-SBD），该模型融合人体姿态的时间轨迹与人体动作的重复模式。通过大量实验验证了所提方法的有效性，并表明其可作为未来研究的潜在基准。