Skeleton-based Action Recognition for Human-Robot Interaction using Self-Attention Mechanism

Motion prediction and action recognition play an influential role in the enhancement of interactions between humans and robots. We aim to predict motions and recognize actions for an interaction-based supermarket assistance scenario. Skeleton-based prediction of human motion and action recognition methods gained a lot of attention with the help of recurrent neural networks, convolutional neural networks, and graph convolutions. For recognition of actions, most of the proposed architectures rely on the predefined structure of the skeleton. In this work, we introduce a new small-scale dataset with actions that are possible in a supermarket interaction scenario. we propose two different self-attention-based models for recognition of actions for learning long-range correlations that do not rely on a predefined skeleton structure. We evaluate the models with extensive experiments containing specific input feature encodings that enhances the motion or trajectory features for accurate prediction and recognition of actions. We validate the effectiveness of the models on the actions in supermarket dataset and a standard benchmark dataset for action recognition known as the NTU RGB+D dataset.