.png){kind=icon}
Return
Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3271593.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Human pose estimation has drawn extensive attention recently and there has been significant progress on it due to the rising popularity of convolutional neural networks (CNN). However, existing state-of-the-art approaches suffer from occlusion, complicated backgrounds, and substantial position fluctuations because of disregarding the human body form. Human parsing is a very pertinent activity that can provide crucial semantic data about bodily parts for position estimation. To overcome the aforesaid limitations, this paper introduces a human pose estimation method using a group-based convolutional neural network model. The proposed method adopts a bottom-up parsing strategy that yields features to extract skeletal key points in the human body. Moreover, it creates a grouping of anatomical key points for individuals by utilizing the non-parametric description for the key point association vector field. Experimental results indicate that the proposed method provides superior performance than the state-of-the-art algorithms in terms of accuracy. In addition, it optimizes its output and detects occluded as well as invisible key points by incorporating feature representation. The proposed method surpasses the recent methods, achieving 93% of the mean average accuracy.
Keywords:
Pose estimation
Convolutional neural networks
Solid modeling
Biological system modeling
Location awareness
Feature extraction
convolutional neural network
occlusion
bottom-up parsing
skeletal keypoint
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
ShareSaveDetection of Non-Stationary GW Signals in High Noise From Cohen's Class of Time-Frequency Representations Using Deep Learning.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSaveBird community structure and dynamics during spring migration in selected habitats of northern Italy
OAAI
OAAIShareSaveAn Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation NetworkSENSORSOAAI
SENSORSIF3.5
OAAIShareSaveTwo-dimensional image transmission through a single optical fiber by wavelength-time multiplexing
PREAI
ShareSave
View more