In this paper, we propose a motion model that focuses on the discriminative parts of the human body related to target motions to classify human motions into specific categories, and apply this model to multi-class daily motion classifications. We extend this model to a motion recognition system which generates multiple sentences associated with human motions. The motion model is evaluated with the following four datasets acquired by a Kinect sensor or multiple infrared cameras in a motion capture studio: UCF-kinect; UT-kinect; HDM05-mocap; and YNL-mocap. We also evaluate the sentences generated from the dataset of motion and language pairs. The experimental results indicate that the motion model improves classification accuracy and our approach is better than other state-of-the-art methods for specific datasets, including human–object interactions with variations in the duration of motions, such as daily human motions. We achieve a classification rate of 81.1% for multi-class daily motion classifications in a non cross-subject setting. Additionally, the sentences generated by the motion recognition system are semantically and syntactically appropriate for the description of the target motion, which may lead to human–robot interaction using natural language.

中文翻译：

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使用判别性身体部位和句子描述对多类日常人体运动进行分类

在本文中，我们提出了一种运动模型，该模型侧重于与目标运动相关的人体有辨别力的部分，将人体运动分类为特定类别，并将该模型应用于多类日常运动分类。我们将此模型扩展到运动识别系统，该系统生成与人体运动相关的多个句子。运动模型使用以下四个数据集进行评估，这些数据集由运动捕捉工作室中的 Kinect 传感器或多个红外摄像机获取：UCF-kinect；UT-kinect; HDM05-mocap; 和 YNL-mocap。我们还评估了从运动和语言对数据集生成的句子。实验结果表明，运动模型提高了分类精度，我们的方法优于其他特定数据集的最新方法，包括具有运动持续时间变化的人与物体的交互，例如日常人体运动。我们在非跨学科设置中实现了 81.1% 的多类日常运动分类的分类率。此外，运动识别系统生成的句子在语义和句法上都适合描述目标运动，这可能会导致使用自然语言的人机交互。