Localizing Moments of Actions in Untrimmed Videos of Infants with Autism
  Spectrum Disorder
Halil Ismail Helvaci Sen-ching Samson Cheung Chen-Nee Chuah Sally Ozonoff
Autism Spectrum Disorder (ASD) presents significant challenges in early
diagnosis and intervention, impacting children and their families. With
prevalence rates rising, there is a critical need for accessible and efficient
screening tools. Leveraging machine learning (ML) techniques, in particular
Temporal Action Localization (TAL), holds promise for automating ASD screening.
This paper introduces a self-attention based TAL model designed to identify
ASD-related behaviors in infant videos. Unlike existing methods, our approach
simplifies complex modeling and emphasizes efficiency, which is essential for
practical deployment in real-world scenarios. Importantly, this work
underscores the importance of developing computer vision methods capable of
operating in naturilistic environments with little equipment control,
addressing key challenges in ASD screening. This study is the first to conduct
end-to-end temporal action localization in untrimmed videos of infants with
ASD, offering promising avenues for early intervention and support. We report
baseline results of behavior detection using our TAL model. We achieve 70%
accuracy for look face, 79% accuracy for look object, 72% for smile and 65% for
vocalization.