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:对自闭症谱系病症的早期干预和护理而言,监测和分析陈规定型行为十分重要。本文件侧重于自动检测计算机视觉技术的陈规定型行为。现成方法通过监督分类和活动识别技术来应对这项任务。然而,无限制的陈规定型行为类型和难以收集自闭症病人的录像记录,在很大程度上限制了现有受监督的检测方法的可行性。因此,我们从新的角度,即未经监督的为发现陈规定型行为而探测视频异常现象的新角度来应对这些挑战。这些模型可以在无标签的录像中进行训练,这些录像只包含正常行为,在推断过程中可以发现未知的异常行为类型。相应,我们根据人造物的时间轨迹和人类行动的重复模式,提出了一种双流深的陈规定型行为侦测模型。我们进行了广泛的实验,以核实我们拟议方法的有效性,并建议将其作为未来研究的潜在基准。</s>