  Human action recognition as an important application of computer vision has
been studied for decades. Among various approaches, skeleton-based methods
recently attract increasing attention due to their robust and superior
performance. However, existing skeleton-based methods ignore the potential
action relationships between different persons, while the action of a person is
highly likely to be impacted by another person especially in complex events. In
this paper, we propose a novel group-skeleton-based human action recognition
method in complex events. This method first utilizes multi-scale
spatial-temporal graph convolutional networks (MS-G3Ds) to extract skeleton
features from multiple persons. In addition to the traditional key point
coordinates, we also input the key point speed values to the networks for
better performance. Then we use multilayer perceptrons (MLPs) to embed the
distance values between the reference person and other persons into the
extracted features. Lastly, all the features are fed into another MS-G3D for
feature fusion and classification. For avoiding class imbalance problems, the
networks are trained with a focal loss. The proposed algorithm is also our
solution for the Large-scale Human-centric Video Analysis in Complex Events
Challenge. Results on the HiEve dataset show that our method can give superior
performance compared to other state-of-the-art methods.
