移動ロボットのための背景・カメラ動きに頑健な行動認識
Action Recognition: From Static Datasets to Moving Robots
カメラ移動や背景の影響を受けずに人間の行動を認識するため、行動領域提案とConvNetを組み合わせた手法を提案し、ロボット搭載や職場安全の異常検知で有効性を示した。
著者: Fahimeh Rezazadegan, Sareh Shirazi, Ben Upcroft, Michael Milford
分類: cs.RO, cs.CV
原文アブストラクト
Deep learning models have achieved state-of-the- art performance in recognizing human activities, but often rely on utilizing background cues present in typical computer vision datasets that predominantly have a stationary camera. If these models are to be employed by autonomous robots in real world environments, they must be adapted to perform independently of background cues and camera motion effects. To address these challenges, we propose a new method that firstly generates generic action region proposals with good potential to locate one human action in unconstrained videos regardless of camera motion and then uses action proposals to extract and classify effective shape and motion features by a ConvNet framework. In a range of experiments, we demonstrate that by actively proposing action regions during both training and testing, state-of-the-art or better performance is achieved on benchmarks. We show the outperformance of our approach compared to the state-of-the-art in two new datasets; one emphasizes on irrelevant background, the other highlights the camera motion. We also validate our action recognition method in an abnormal behavior detection scenario to improve workplace safety. The results verify a higher success rate for our method due to the ability of our system to recognize human actions regardless of environment and camera motion.
