A Spatial-Temporal Graph Convolutional Networks-based Approach for the OpenPack Challenge 2022.

We report the proposed method of Team Ritsumei for the OpenPack challenge 2022. In this work, we proposed to use a motion-aware and temporal-enhanced Spatial-Temporal Graph Convolutional Networks for the representation of the keypoint modality features. We also leverage the Accelerometer and Gyroscope modality as auxiliary modalities to improve the performance. Our final result is based on the fusion of four modalities. We report the 92.32% F1 score on the submission set, which won the 3rd place in the OpenPack challenge 2022.