CSSE Seminar Series (CSSESS)
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Seminar
Quantifying and Recognising Human Movement Patterns from Monocular Video Images
Spatial and temporal segmentation of continuous motion with application to quantifying Parkinsonism gait, anthropometric biometrics and gait signatures.
Speaker: Dr. Richard Green.
Institute: Computer Science Department, University of Canterbury.
Time/Place: 10:00 AM, Wed, 16 April, in Room 031, Erskine Building.
Abstract
Computer vision research into tracking and recognising human movement has so far been mostly limited to gait or frontal posing. This research presents a framework for the spatial and temporal segmentation of continuous motion to quantify and recognise a diverse range of 3D movement skills from gait to saltos. A novel 3D colour body model is accurately sized and texture mapped to each person for more robust tracking. Tracking is further stabilised by estimating the joint angles for the next frame using a forward smoothing Particle Filter with the search space optimised utilising feedback from an Automatic Movement Recognition (AMR) system. A new paradigm enables the temporal segmentation of continuous motion into dynemes for the deconstruction of hundreds of movement skills. Using HMM, the AMR system attempts to infer the human movement skill that could have produced the observed sequence of dynemes. This AMR system, free of markers and set-up procedures, successfully quantifies biomechanical components and recognises hundreds of movement skills. It has also been applied to quantify the Parkinsonian movement disorder and enable biometric identification from gait and a novel anthropometric signature.
Biography
Richard Green has just completed his PhD in computer vision at the University of Sydney. He developed a novel system which tracked and recognized human movement, Parkinson's Disease and biometric identification. Richard completed an ME at the University of Canterbury in 1995 where he enhanced an expert system to analyse electroencephalograms in real-time to enable immediate feedback of epileptiform activity for long term monitoring using a hybrid of artificial neural networks and high level languages. During his BSc from the University of Sydney he developed a computer vision based system to track eye movement for disabled human-computer interaction.
His research interests include computer vision, human-computer interaction, cognitive science, software engineering, commercialisation, multimedia, teaching, biomechanics, gymnastics, skiing, snow-boarding and coaching.
Richard's business experience includes managing his own businesses in internet marketing, computer vision products and banking software with 50 staff culminating in an international product sold to a major multinational company.
Richard competed for New Zealand in gymnastics having come second in Men's gymnastics and has coached and judged Olympic level gymnastics both here and overseas.
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