時空間畳み込みネットワークによる屋内物体操作動作のセグメンテーションを用いた人間工学的リスク予測に向けて
Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks
RGB-D動画から動作を時空間畳み込みネットワークでセグメンテーションし、物体操作時の人間工学的リスク（安全・要注意・即対応）をリアルタイム予測する手法を提案。新規データセットで87-94%のF1スコアを達成。
著者: Behnoosh Parsa, Ekta U. Samani, Rose Hendrix, Cameron Devine, Shashi M. Singh, Santosh Devasia, Ashis G. Banerjee
分類: cs.RO, cs.CV
原文アブストラクト
Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonomically safe, require monitoring, or need immediate attention. For performance evaluation, in addition to an open-source kitchen dataset, we collected a new dataset comprising twenty individuals picking up and placing objects of varying weights to and from cabinet and table locations at various heights. Results show very high (87-94)\% F1 overlap scores among the ground truth and predicted frame labels for videos lasting over two minutes and consisting of a large number of actions.
