  We present a new algorithm for selection of informative frames in video
action recognition. Our approach is designed for aerial videos captured using a
moving camera where human actors occupy a small spatial resolution of video
frames. Our algorithm utilizes the motion bias within aerial videos, which
enables the selection of motion-salient frames. We introduce the concept of
patch mutual information (PMI) score to quantify the motion bias between
adjacent frames, by measuring the similarity of patches. We use this score to
assess the amount of discriminative motion information contained in one frame
relative to another. We present an adaptive frame selection strategy using
shifted leaky ReLu and cumulative distribution function, which ensures that the
sampled frames comprehensively cover all the essential segments with high
motion salience. Our approach can be integrated with any action recognition
model to enhance its accuracy. In practice, our method achieves a relative
improvement of 2.2 - 13.8% in top-1 accuracy on UAV-Human, 6.8% on NEC Drone,
and 9.0% on Diving48 datasets.
