A learning-based markerless approach for full-body kinematics estimation in-natura from a single image
We present a supervised machine learning approach for markerless estimation of human full-body kinematics for a cyclist from an unconstrained colour image. This approach is motivated by the limitations of existing marker-based approaches restricted by infrastructure, environmental conditions, and obtrusive markers. By using a discriminatively learned mixture-of-parts model, we construct a probabilistic tree representation to model the configuration and appearance of human body joints. During...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of biomechanics|
|01_Drory_A_Learning-based_markerless_2017.pdf||2.58 MB||Adobe PDF||Request a copy|
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