Detecting Self-Stimulatory Behaviours for Autism Diagnosis


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Autism Spectrum Disorders (ASD), often referred to as autism, are neurological disorders characterised by deficits in cognitive skills, social and communicative behaviours. A common way of diagnosing ASD is by studying behavioural cues expressed by the children. An algorithm for detecting three types of self-stimulatory behaviours from publicly available unconstrained videos is proposed here. The child’s body is tracked in the video by a careful selection of poselet bounding box predictions using a nearest neighbour algorithm. A global motion descriptor – Histogram of Dominant Motions (HDM) – is computed using the dominant motion flow in the detected body regions. The motion model built using this descriptor is used for detecting the self-stimulatory behaviours. Experiments conducted on the recently released unconstrained SSBD video dataset show significant improvement in detection accuracy over the baseline approach. The robustness of the method is validated using benchmark action recognition datasets. The proposed poselet bounding box selection algorithm is validated against the ground truth annotation data provided with the UCF101 dataset.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
Place of PublicationParis, France
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages15
ISBN (Electronic)9781479957514
ISBN (Print)9781479957514
Publication statusPublished - 2014
EventIEEE International Conference on Image Processing 2014 - Paris, Paris, France
Duration: 27 Oct 201430 Oct 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014


ConferenceIEEE International Conference on Image Processing 2014


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