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The early detection of developmental disorders is key to child outcome,allowing interventions to be initiated that promote development and improveprognosis. Research on autism spectrum disorder (ASD) suggests behavioralmarkers can be observed late in the first year of life. Many of these studiesinvolved extensive frame-by-frame video observation and analysis of a child'snatural behavior. Although non-intrusive, these methods are extremelytime-intensive and require a high level of observer training; thus, they areimpractical for clinical and large population research purposes. Diagnosticmeasures for ASD are available for infants but are only accurate when used byspecialists experienced in early diagnosis. This work is a first milestone in along-term multidisciplinary project that aims at helping clinicians and generalpractitioners accomplish this early detection/measurement task automatically.We focus on providing computer vision tools to measure and identify ASDbehavioral markers based on components of the Autism Observation Scale forInfants (AOSI). In particular, we develop algorithms to measure three criticalAOSI activities that assess visual attention. We augment these AOSI activitieswith an additional test that analyzes asymmetrical patterns in unsupportedgait. The first set of algorithms involves assessing head motion by trackingfacial features, while the gait analysis relies on joint foregroundsegmentation and 2D body pose estimation in video. We show results that provideinsightful knowledge to augment the clinician's behavioral observationsobtained from real in-clinic assessments.

Computer vision tools for the non-invasive assessment of autism-related behavioral markers