Pose And Joint-Aware Action Recognition

Most human action recognition systems typically consider static appearances and motion as independent streams of information. In this paper, we consider the evolution of human pose and propose a method to better capture interdependence among skeleton joints. Our model extracts motion information from each joint independently, reweighs the information and finally performs inter-joint reasoning. The effectiveness of pose and joint-based representations is strengthened using a geometry-aware data augmentation technique which jitters pose heatmaps while retaining the dynamics of the action. Our best model gives an absolute improvement of 8.19% on JHMDB, 4.31% on HMDB and 1.55 mAP on Charades datasets over state-of-the-art methods using pose heat-maps alone. Fusing with RGB and flow streams leads to improvement over state-of-the-art. Our model also outperforms the baseline on Mimetics, a dataset with out-of-context videos by 1.14% while using only pose heatmaps. Further, to filter out clips irrelevant for action recognition, we re-purpose our model for clip selection guided by pose information and show improved performance using fewer clips.