Detecting Self-Stimulatory Behaviours for Autism Diagnosis

Shyam RAJAGOPALAN, Roland GOECKE

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    8 Citations (Scopus)
    4 Downloads (Pure)

    Abstract

    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
    Place of PublicationParis, France
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages564-578
    Number of pages15
    ISBN (Print)9781479957514
    DOIs
    Publication statusPublished - 2014
    Event2014 IEEE International Conference on Image Processing - Paris, Paris, France
    Duration: 27 Oct 201430 Oct 2014

    Conference

    Conference2014 IEEE International Conference on Image Processing
    CountryFrance
    CityParis
    Period27/10/1430/10/14

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    Cite this

    RAJAGOPALAN, S., & GOECKE, R. (2014). Detecting Self-Stimulatory Behaviours for Autism Diagnosis. In 2014 IEEE International Conference on Image Processing (pp. 564-578). Paris, France: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICIP.2014.7025294
    RAJAGOPALAN, Shyam ; GOECKE, Roland. / Detecting Self-Stimulatory Behaviours for Autism Diagnosis. 2014 IEEE International Conference on Image Processing. Paris, France : IEEE, Institute of Electrical and Electronics Engineers, 2014. pp. 564-578
    @inproceedings{2ad16d7ad6b64fcf8e23bbe019af447a,
    title = "Detecting Self-Stimulatory Behaviours for Autism Diagnosis",
    abstract = "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.",
    keywords = "Human Action Recognition, Autism diagnosis",
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    RAJAGOPALAN, S & GOECKE, R 2014, Detecting Self-Stimulatory Behaviours for Autism Diagnosis. in 2014 IEEE International Conference on Image Processing. IEEE, Institute of Electrical and Electronics Engineers, Paris, France, pp. 564-578, 2014 IEEE International Conference on Image Processing, Paris, France, 27/10/14. https://doi.org/10.1109/ICIP.2014.7025294

    Detecting Self-Stimulatory Behaviours for Autism Diagnosis. / RAJAGOPALAN, Shyam; GOECKE, Roland.

    2014 IEEE International Conference on Image Processing. Paris, France : IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 564-578.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AU - GOECKE, Roland

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    RAJAGOPALAN S, GOECKE R. Detecting Self-Stimulatory Behaviours for Autism Diagnosis. In 2014 IEEE International Conference on Image Processing. Paris, France: IEEE, Institute of Electrical and Electronics Engineers. 2014. p. 564-578 https://doi.org/10.1109/ICIP.2014.7025294