  We present ActionXPose, a novel 2D pose-based algorithm for posture-level
Human Action Recognition (HAR). The proposed approach exploits 2D human poses
provided by OpenPose detector from RGB videos. ActionXPose aims to process
poses data to be provided to a Long Short-Term Memory Neural Network and to a
1D Convolutional Neural Network, which solve the classification problem.
ActionXPose is one of the first algorithms that exploits 2D human poses for
HAR. The algorithm has real-time performance and it is robust to camera
movings, subject proximity changes, viewpoint changes, subject appearance
changes and provide high generalization degree. In fact, extensive simulations
show that ActionXPose can be successfully trained using different datasets at
once. State-of-the-art performance on popular datasets for posture-related HAR
problems (i3DPost, KTH) are provided and results are compared with those
obtained by other methods, including the selected ActionXPose baseline.
Moreover, we also proposed two novel datasets called MPOSE and ISLD recorded in
our Intelligent Sensing Lab, to show ActionXPose generalization performance.
