Recognizing Human Pose and Actions for Interactive Robots

Abstract

We have presented a neuro-inspired method for monocular tracking and action recognition for movement imitation. Our approach combines vocabularies of kinematic motion learned offline with online estimation of a demonstrator's underlying virtual trajectory. A modular approach to pose estimation is taken for computational tractability and emulation of structures hypothesized in neuroscience. Our current results suggest our method can perform tracking and recognition from partial observations at interactive rates. Our current system demonstrates robustness with respect to the viewpoint of the camera, the speed of performance of the action, and recovery from ambiguous situations.