Markerless Human Motion Analysis from RGB videos


"Markerless Human Motion Analysis from RGB videos"
Matteo Moro
Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. Nowadays the study of human motion is commonly done through marker-based techniques and motion capture systems. If from one side these methods are precise and reliable, from the other they present some disadvantages, in particular they are expensive, invasive and time consuming. For these reasons in the last years the research of cheaper and easier markerless techniques had made a lot of progress. In particular we are witnessing a steady growth in the design and implementation of computer vision and machine learning algorithms which can be applied to markerless human motion analysis. This type of analysis may facilitate the extraction of features that give qualitative and quantitative information about human motion and that can be used for classification tasks (for example healthy vs unhealthy movements). We implement a markerless pipeline based on state of the art algorithms (DeepLabCut) and apply it to different case studies. In this talk we will show some preliminary results.
Short Bio:
Matteo Moro is a first year PhD student in Computer Science. He obtained his BSc in Biomedical Engineering and his MSc in Neuroengineering and bio-ICT, both at the University of Genova. His research interests are in the field of Computer Vision and Deep Learning. His current research is focused on pose estimation and makerless human motion analysis.
Thursday, 7th November 2019, 2:30PM
DIBRIS, Valletta Puggia, Conference Hall, III floor - Via Dodecaneso 35, Genova 


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