A real-time visual action-recognition framework for time-varying-geometry objects

This paper develops a real-world, real-time sensing-system reconfiguration framework for time-varying geometry (TVG) object action recognition. Currently, a method specifically tailored for sensing such objects, especially humans, does not exist. A 10-level superscalar pipeline structure is proposed to address real-time concerns. Carefully designed evaluation criteria are used with real-world experiments to validate the method. Overall, it was concluded that this implementation (of our more generic framework) was successful in its task-specific goals, and that the framework can be applied to many other TVG object action sensing tasks.