Real-time human activity recognition plays an essential role in real-world human-centered robotics applications, such as assisted living and human-robot collaboration. Although previous methods based on skeletal data to encode human poses showed promising results on real-time activity recognition, they lacked the capability to consider the context provided by objects within the scene and in use by the humans, which can provide a further discriminant between human activity categories. In this paper, we propose a novel approach to real-time human activity recognition, through simultaneously learning from observations of both human poses and objects involved in the human activity. We formulate human activity recognition as a joint optimization problem under a unified mathematical framework, which uses a regression-like loss function to integrate human pose and object cues and defines structured sparsity-inducing norms to identify discriminative body joints and object attributes. To evaluate our method, we perform extensive experiments on two benchmark datasets and a physical robot in a home assistance setting. Experimental results have shown that our method outperforms previous methods and obtains real-time performance for human activity recognition with a processing speed of 10^4 Hz.

中文翻译：

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从人体姿势和物体线索中同时学习以进行实时活动识别

实时人类活动识别在现实世界中以人为中心的机器人应用中起着至关重要的作用，例如辅助生活和人机协作。尽管以前基于骨骼数据对人体姿势进行编码的方法在实时活动识别方面显示出有希望的结果，但它们缺乏考虑场景中物体提供的上下文和人类使用的上下文的能力，这可以进一步区分人类活动类别。在本文中，我们提出了一种实时人类活动识别的新方法，通过同时从人类活动中涉及的人体姿势和物体的观察中学习。我们将人类活动识别制定为统一数学框架下的联合优化问题，它使用类似回归的损失函数来整合人体姿势和物体线索，并定义结构化的稀疏诱导规范来识别有区别的身体关节和物体属性。为了评估我们的方法，我们在家庭辅助设置中对两个基准数据集和一个物理机器人进行了大量实验。实验结果表明，我们的方法优于以前的方法，并以10^4Hz的处理速度获得了人类活动识别的实时性能。