论文标题
在HRI应用中，用于在线人类活动识别的紧凑序列编码方案
A compact sequence encoding scheme for online human activity recognition in HRI applications
论文作者
论文摘要
人类活动的识别和分析一直是模式识别和机器智能中最活跃的领域之一，其应用在各个领域，包括但不限于劳动游戏，监视，体育分析和医疗保健。尤其是在人类机器人互动中，人类活动的理解起着至关重要的作用，因为家庭机器人助手是不久的将来的趋势。但是，可以支持复杂机器智能任务的最新基础架构并不总是可用，并且可能不适合普通消费者，因为机器人硬件很昂贵。在本文中，我们提出了一种新型的动作序列编码方案，该方案有效地使用基于Mahalanobis距离的形状特征和radon变换，将时空动作序列有效地转换为紧凑的表示。该表示形式可以用作轻量级卷积神经网络的输入。实验表明，拟议的管道基于最先进的人姿势估计技术可以提供强大的端到端在线操作识别方案，该方案可在缺乏极端计算能力的硬件上部署。
Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as household robotic assistants are a trend of the near future. However, state-of-the-art infrastructures that can support complex machine intelligence tasks are not always available, and may not be for the average consumer, as robotic hardware is expensive. In this paper we propose a novel action sequence encoding scheme which efficiently transforms spatio-temporal action sequences into compact representations, using Mahalanobis distance-based shape features and the Radon transform. This representation can be used as input for a lightweight convolutional neural network. Experiments show that the proposed pipeline, when based on state-of-the-art human pose estimation techniques, can provide a robust end-to-end online action recognition scheme, deployable on hardware lacking extreme computing capabilities.
