.png){kind=icon}
返回
Segmentation-Based Background-Inference and Small-Person Pose Estimation
.png){kind=icon}
OA
AI
DOI：10.1109/LSP.2022.3186594.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Despite encouraging results have been achieved in human pose estimation in recent years, it remains challenging problems. When the background is similar to the human body parts, and there are small persons with low-resolution in the image, the performance may degrade dramatically. This paper addresses problems in background-inference and small-person pose estimation. To achieve this, a novel pose estimation algorithm is proposed on the basis of person semantic segmentation deep neural network. Different from most previous methods with a single pose estimation model, we generate mixture models with pose estimation and semantic segmentation. We introduce novel generative adversarial model and auxiliary model to realize the semantic segmentation network, which can handle the confusion of the similar regions in the background. In addition, to address the problem of the scale differences between big and small persons' keypoints, we add additional position and channel attention modules to the first two stages of OpenPose. We conduct extensive experiments on COCO and VOC datasets. And we compare the proposed method with the most popular state-of-the-art human pose estimation and semantic segmentation frameworks, including MultiPoseNet, Deterton2 and DeepLab V3. Our experimental results show that the proposed method is more accurate than the state-of-the-art algorithms and performs effectively in tackling the complex situations.
Keyword:
Pose estimation
Semantics
Convolution
Neural networks
Image segmentation
Training
Deep learning
Pose estimation
deep neural network
semantic segmentation
background-inference
small-person
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
Electrochemically assisted micro localized grafting of aptamers in a microchannel engraved in fluorinated thermoplastic polymer Dyneon THV.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Agile Innovation to transform healthcare: innovating in complex adaptive systems is an everyday process, not a light bulb event改变医疗保健的敏捷创新: 在复杂的自适应系统中进行创新是日常过程，而不是灯泡事件OAAI
OAAI分享收藏
查看更多内容