AZTR: 自動ズームと時間推論による空中映像行動認識
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning
ドローン映像から人間を自動ズームで捉え、時間推論で行動を認識する軽量手法を提案し、エッジ機器でも高精度を実現した。
著者: Xijun Wang, Ruiqi Xian, Tianrui Guan, Celso M. de Melo, Stephen M. Nogar, Aniket Bera, Dinesh Manocha
分類: cs.CV, cs.RO
原文アブストラクト
We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately. This makes it easier to extract the key features and reduces the computational overhead. We also present an efficient temporal reasoning algorithm to capture the action information along the spatial and temporal domains within a controllable computational cost. Our approach has been implemented and evaluated both on the desktop with high-end GPUs and on the low power Robotics RB5 Platform for robots and drones. In practice, we achieve 6.1-7.4% improvement over SOTA in Top-1 accuracy on the RoCoG-v2 dataset, 8.3-10.4% improvement on the UAV-Human dataset and 3.2% improvement on the Drone Action dataset.
