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

PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition