  We present a novel approach for action recognition in UAV videos. Our
formulation is designed to handle occlusion and viewpoint changes caused by the
movement of a UAV. We use the concept of mutual information to compute and
align the regions corresponding to human action or motion in the temporal
domain. This enables our recognition model to learn from the key features
associated with the motion. We also propose a novel frame sampling method that
uses joint mutual information to acquire the most informative frame sequence in
UAV videos. We have integrated our approach with X3D and evaluated the
performance on multiple datasets. In practice, we achieve 18.9% improvement in
Top-1 accuracy over current state-of-the-art methods on UAV-Human(Li et al.,
2021), 7.3% improvement on Drone-Action(Perera et al., 2019), and 7.16%
improvement on NEC Drones(Choi et al., 2020).
