@InProceedings{10.20532/ccvw.2014.0002,
  author =       {Kruno Hrvatini{\' c} and Luka Malovan and Frano
                  Petric and Damjan Mikli{\' c} and Zdenko Kova{\v
                  c}i{\' c}},
  title =        {Object Tracking Implementation for a Robot-Assisted
                  Autism Diagnostic Imitation Task},
  booktitle =    {Proceedings of the Croatian Compter Vision Workshop,
                  Year 2},
  pages =        {33-38},
  year =         2014,
  editor =       {Lon{\v c}ari{\' c}, Sven and Suba{\v s}i{\' c},
                  Marko},
  address =      {Zagreb},
  month =        {September},
  organization = {Center of Excellence for Computer Vision},
  publisher =    {University of Zagreb},
  abstract =     {Autism spectrum disorders (ASD) is a term used to
                  describe a range of neurodevelopmental disorders
                  affecting about 1\% of the population, with
                  increasing prevalence. Due to the absence of any
                  physiological markers, diagnostics is based purely
                  on behavioral tests. The diagnostic procedure can in
                  some cases take years to complete, and the outcome
                  depends greatly on the expertise and experience of
                  the clinician. The predictable and consistent
                  behavior and rapidly increasing sensing capabilities
                  of robotic devices have the potential to contribute
                  to a faster and more objective diagnostic
                  procedure. However, significant scientific and
                  technological breakthroughs are needed, particularly
                  in the field of robotic perception, before robots
                  can become useful tools for diagnosing autism. In
                  this paper, we present computer vision algorithms
                  for performing gesture imitation. This is a
                  standardized diagnostic task, usually performed by
                  clinicians, that was implemented on a small-scale
                  humanoid robot. We describe the algorithms used to
                  perform object recognition, grasping, object
                  tracking and gesture evaluation in a clinical
                  setting. We present an analysis of the algorithms in
                  terms of reliability and performance and describe
                  the first clinical trials.},
  doi =          {10.20532/ccvw.2014.0002},
  url =          {https://doi.org/10.20532/ccvw.2014.0002}
}