Athlete 3D pose estimation from a monocular TV sports video using pre-trained temporal convolutional networks

2020 ◽  
Author(s):  
Tomoka Murakami ◽  
Takayuki Nakamura
Keyword(s):  
Pose Estimation ◽  
3D Pose Estimation ◽  
Sports Video ◽  
Convolutional Networks

scholarly journals Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation

2020 ◽  
Author(s):  
Shengyuan Liu ◽  
Pei Lv ◽  
Yuzhen Zhang ◽  
Jie Fu ◽  
Junjin Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Pose Estimation ◽  
State Of The Art ◽  
Human Pose Estimation ◽  
Body Structure ◽  
Single Image ◽  
3D Pose Estimation ◽  
Convolutional Networks ◽  
Human Pose ◽  
Fixed Tree

This paper proposes a novel Semi-Dynamic Hypergraph Neural Network (SD-HNN) to estimate 3D human pose from a single image. SD-HNN adopts hypergraph to represent the human body to effectively exploit the kinematic constrains among adjacent and non-adjacent joints. Specifically, a pose hypergraph in SD-HNN has two components. One is a static hypergraph constructed according to the conventional tree body structure. The other is the semi-dynamic hypergraph representing the dynamic kinematic constrains among different joints. These two hypergraphs are combined together to be trained in an end-to-end fashion. Unlike traditional Graph Convolutional Networks (GCNs) that are based on a fixed tree structure, the SD-HNN can deal with ambiguity in human pose estimation. Experimental results demonstrate that the proposed method achieves state-of-the-art performance both on the Human3.6M and MPI-INF-3DHP datasets.

scholarly journals Feature Boosting Network For 3D Pose Estimation

2020 ◽  
Vol 42 (2) ◽  
pp. 494-501 ◽  
Author(s):  
Jun Liu ◽  
Henghui Ding ◽  
Amir Shahroudy ◽  
Ling-Yu Duan ◽  
Xudong Jiang ◽  
...  
Keyword(s):  
Pose Estimation ◽  
3D Pose Estimation

scholarly journals Gait analysis for early Parkinson’s disease detection based on deep learning

2019 ◽  
Vol 5 (1) ◽  
pp. 9-12
Author(s):  
Jyothsna Kondragunta ◽  
Christian Wiede ◽  
Gangolf Hirtz
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Gait Analysis ◽  
Pose Estimation ◽  
Early Identification ◽  
Human Gait ◽  
3D Pose Estimation ◽  
Wearable Technologies ◽  
Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

Fast and Robust Multi-Person 3D Pose Estimation and Tracking from Multiple Views

2021 ◽  
pp. 1-1
Author(s):  
Junting Dong ◽  
Qi Fang ◽  
Wen Jiang ◽  
Yurou Yang ◽  
Qixing Huang ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Multiple Views ◽  
3D Pose Estimation

3D Pose Estimation Enables Virtual Head Fixation in Freely Moving Rats

2021 ◽  
Author(s):  
Artur Schneider ◽  
Christian Zimmermann ◽  
Mansour Alyahyay ◽  
Thomas Brox ◽  
Ilka Diester
Keyword(s):  
Pose Estimation ◽  
3D Pose Estimation ◽  
Head Fixation ◽  
Freely Moving Rats ◽  
Freely Moving

Respective Volumetric Heatmap Autoencoder for Multi-Person 3D Pose Estimation

2021 ◽  
Author(s):  
Minghao Wang ◽  
Long Ye ◽  
Fei Hu ◽  
Li Fang ◽  
Wei Zhong ◽  
...  
Keyword(s):  
Pose Estimation ◽  
3D Pose Estimation
Sign in / Sign up

Export Citation Format

Share Document