论文标题
分类器的合奏可以在包装活动中提供更好的识别结果吗？
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?
论文作者
论文摘要
长期以来，基于骨架的运动捕获（MOCAP）系统已被广泛用于模仿复杂的人类行动。 MOCAP数据还证明了其在人类活动识别任务中的有效性。但是，对于较小的数据集来说，这是一项非常具有挑战性的任务。缺乏这种工业活动的数据进一步增加了困难。在这项工作中，我们提出了一种基于合奏的机器学习方法，该方法旨在在MOCAP数据集上更好地工作。该实验已经对Bento包装活动识别挑战挑战中给出的MOCAP数据进行。Bento是一个类似于午餐盒的日本单词。首先处理原始MOCAP数据后，我们通过使用建议的集成模型实现了10倍交叉验证的惊人精度，在10倍的交叉验证上达到了98％的精度，而在剩余的越界验证上达到了82％。
Skeleton-based Motion Capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on 10-fold Cross-Validation and 82% on Leave-One-Out-Cross-Validation by using the proposed ensemble model.
