论文标题
仔细研究人类活动识别模型
A Close Look into Human Activity Recognition Models using Deep Learning
论文作者
论文摘要
与更传统的机器学习技术相比，使用深度学习技术的人类活动识别已经变得越来越流行，因为它具有很高的有效性，并且成本相对较低。本文调查了一些基于深度学习结构的最先进的人类活动识别模型，并具有包含卷积神经网络（CNN），长期记忆（LSTM）或多种类型的混合系统的层。分析概述了如何实施模型以最大程度地提高其有效性及其所面临的一些潜在局限性。
Human activity recognition using deep learning techniques has become increasing popular because of its high effectivity with recognizing complex tasks, as well as being relatively low in costs compared to more traditional machine learning techniques. This paper surveys some state-of-the-art human activity recognition models that are based on deep learning architecture and has layers containing Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), or a mix of more than one type for a hybrid system. The analysis outlines how the models are implemented to maximize its effectivity and some of the potential limitations it faces.
