Deep representation learning for marker-less human posture analysis

This thesis presents a holistic human posture analysis system. The proposed system leverages the state-of-the-art deep learning techniques to feature a comprehensive pipeline. Moreover, a new nonlinear computational layer is proposed to the deep convolutional neural network architectures to incorporate human perception capabilities into the deep learning architectures.