MITFAS: 相互情報量に基づく時間的特徴アライメントとサンプリングによるUAV動画行動認識
MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action Recognition
UAV動画の行動認識において、相互情報量を用いて時間方向の特徴をアライメントし、情報量の多いフレームを選択する手法を提案し、複数データセットで精度を向上させた。
著者: Ruiqi Xian, Xijun Wang, Dinesh Manocha
分類: cs.CV, cs.RO
原文アブストラクト
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).
