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We present ActionXPose, a novel 2D pose-based algorithm for posture-levelHuman Action Recognition (HAR). The proposed approach exploits 2D human posesprovided by OpenPose detector from RGB videos. ActionXPose aims to processposes data to be provided to a Long Short-Term Memory Neural Network and to a1D Convolutional Neural Network, which solve the classification problem.ActionXPose is one of the first algorithms that exploits 2D human poses forHAR. The algorithm has real-time performance and it is robust to cameramovings, subject proximity changes, viewpoint changes, subject appearancechanges and provide high generalization degree. In fact, extensive simulationsshow that ActionXPose can be successfully trained using different datasets atonce. State-of-the-art performance on popular datasets for posture-related HARproblems (i3DPost, KTH) are provided and results are compared with thoseobtained by other methods, including the selected ActionXPose baseline.Moreover, we also proposed two novel datasets called MPOSE and ISLD recorded inour Intelligent Sensing Lab, to show ActionXPose generalization performance.

ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition