Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism
This addresses the challenge of monitoring ASD behaviors for early intervention, but it is incremental as it adapts unsupervised anomaly detection to a specific domain.
The paper tackles the problem of automatically detecting stereotypical behaviors in Autism Spectrum Disorder (ASD) using unsupervised video anomaly detection, proposing a dual-stream deep model (DS-SBD) that achieves effectiveness as a potential benchmark.
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.