# Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism

## TL;DR

This paper introduces an unsupervised deep learning approach for detecting stereotypical behaviors in autism using video analysis, addressing the challenge of limited labeled data and unbounded behavior types.

## Contribution

The paper presents DS-SBD, a novel dual-stream deep model that detects abnormal behaviors in videos without requiring labeled abnormal data, focusing on pose trajectories and action repetition patterns.

## Key findings

- Effective detection of stereotypical behaviors in unlabeled videos
- Outperforms supervised methods in certain scenarios
- Proposes a new benchmark for autism behavior analysis

## Abstract

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.