.png){kind=icon}
返回
Improved Convolutional Pose Machines for Human Pose Estimation Using Image Sensor Data
.png){kind=icon}
OA
AI
DOI：10.3390/s19030718.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, increasing human data comes from image sensors. In this paper, a novel approach combining convolutional pose machines (CPMs) with GoogLeNet is proposed for human pose estimation using image sensor data. The first stage of the CPMs directly generates a response map of each human skeleton's key points from images, in which we introduce some layers from the GoogLeNet. On the one hand, the improved model uses deeper network layers and more complex network structures to enhance the ability of low level feature extraction. On the other hand, the improved model applies a fine-tuning strategy, which benefits the estimation accuracy. Moreover, we introduce the inception structure to greatly reduce parameters of the model, which reduces the convergence time significantly. Extensive experiments on several datasets show that the improved model outperforms most mainstream models in accuracy and training time. The prediction efficiency of the improved model is improved by 1.023 times compared with the CPMs. At the same time, the training time of the improved model is reduced 3.414 times. This paper presents a new idea for future research.
Keyword:
human pose estimation
convolutional pose machines
GoogLeNet
fine-tuning
image sensor
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Human Pose Estimation from Monocular Images: A Comprehensive Survey从单目图像中估计人体姿态: 综合调查.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Structural study of lanthanides(III) in aqueous nitrate and chloride solutions by EXAFS通过EXAFS对硝酸盐和氯化物水溶液中镧系元素 (III) 的结构研究
PREAI
分享收藏Mechanisms of antiwear tribofilm growth revealed in situ by single-asperity sliding contacts单凹凸滑动接触原位揭示的抗磨摩擦膜生长机理ScienceOAAI
ScienceIF0
OAAI分享收藏
查看更多内容