Semantic-Augmented Local Decision Aggregation Network for Action Recognition

It is challenging for an intelligent system to recognize the actions recorded in an RGB video due to the large amount of information and wide variations in the RGB video. On the other side, skeleton data focuses on the region of human body but lacks the interaction information with the background, which is complementary to the RGB data. Recently, some works focus on combining the RGB and skeleton data together to boost the performance of action recognition. However, the semantic information between joints is missing in existing works, which is important for action recognition. In this paper, we propose a novel semantic-augmented local decision aggregation network for action recognition. Specifically, we regard the area of body joints as the attention region to extract a local spatio-temporal feature for each body joint. In order to take advantage of the semantic information between joints, we propose a semantic information module, which jointly encodes the spatial and temporal index of body joints to enhance the representation ability of the local features. For better learning ability, instead of aggregating the local features, we first make decisions based on each individual local feature and then aggregate the local decisions for final recognition, which reflects the idea of resemble learning. Extensive experiments demonstrate the effectiveness of our proposed module which improves the performance of action recognition on three commonly used datasets.