Human Pose Estimation from Monocular Images

2020 ◽  
Author(s):  
Bastian Wandt
Keyword(s):  
3D Reconstruction ◽  
Human Body ◽  
Main Idea ◽  
Human Motion ◽  
Training Data ◽  
Human Body Model ◽  
Body Model ◽  
Temporal Properties ◽  
Human Pose ◽  
Human Motions

Abstract This dissertation deals with the problem of capturing human motions and poses using a single camera. The frst part of the thesis proposes two closely related approaches for the 3D reconstruction of human motions from image sequences. To resolve inherent ambiguities in monocular 3D reconstruction the main idea of this part is to exploit temporal properties of human motions in combination with a human body model learned from training data. The second part of the thesis tackles the problem of reconstructing a human pose from a single image. A human body model is learned by training a deep neural network that covers nonlinearities and anthropometric constraints. C O N T E N T S 1 Introduction ….. 1 1.1 Applications and Commercial Systems . . . . . . . . . . . 1 1.2 Image-based Motion Capture . . . . . . . . . . . . . . . . 2 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.1 Time Consistent Human Motion Reconstruction . 6 1.3.2 RepNet . . . . . . . ...

scholarly journals Computer Vision Intelligent Approaches to Extract Human Pose and Its Activity from Image Sequences

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 159
Author(s):  
Paulo J. S. Gonçalves ◽  
Bernardo Lourenço ◽  
Samuel Santos ◽  
Rodolphe Barlogis ◽  
Alexandre Misson
Keyword(s):  
Computer Vision ◽  
Human Body ◽  
Prediction Models ◽  
Image Sequences ◽  
Video Frame ◽  
Support Vector ◽  
Training Phase ◽  
Human Body Model ◽  
Body Model ◽  
Human Pose

The purpose of this work is to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), support vector machines (SVM) and long short-term memory networks (LSTM) to predict human pose and activity from image sequences, based on computer vision approaches to gather the required features. To obtain the human pose semantics (output classes), based on a set of 3D points that describe the human body model (the input variables of the predictive model), prediction models were obtained from the acquired data, for example, video images. In the same way, to predict the semantics of the atomic activities that compose an activity, based again in the human body model extracted at each video frame, prediction models were learned using LSTM networks. In both cases the best learned models were implemented in an application to test the systems. The SVM model obtained 95.97% of correct classification of the six different human poses tackled in this work, during tests in different situations from the training phase. The implemented LSTM learned model achieved an overall accuracy of 88%, during tests in different situations from the training phase. These results demonstrate the validity of both approaches to predict human pose and activity from image sequences. Moreover, the system is capable of obtaining the atomic activities and quantifying the time interval in which each activity takes place.

scholarly journals Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhesen Chu ◽  
Min Li
Keyword(s):  
Time Series ◽  
Human Body ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Human Body Model ◽  
Motion Data ◽  
Body Model ◽  
Lstm Network

This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.

scholarly journals 3D reconstruction of human body model based on incremental Motion recovery structure

2022 ◽  
Vol 355 ◽  
pp. 03026
Author(s):  
Shiheng Zhang ◽  
Shaopeng Zhang ◽  
Jianyang Chen ◽  
Xiuling Wang
Keyword(s):  
3D Reconstruction ◽  
Human Body ◽  
Depth Map ◽  
Research Direction ◽  
Human Body Model ◽  
Motion Recovery ◽  
Body Model ◽  
Model Based ◽  
Important Research Topic ◽  
Reconstructed Model

3D reconstruction of human body model is a very important research topic in 3D reconstruction and also a challenging research direction in engineering field. In this paper, the whole pipeline flow of 3D reconstruction of human body model based on incremental motion recovery structure is proposed. Use mobile phone to collect images from different angles and screen them; Secondly, feature extraction and matching under SIFT operator, sparse reconstruction of incremental motion recovery structure, dense reconstruction based on depth map and other processes are carried out. Poisson surface reconstruction is finally carried out to achieve model reconstruction. Experiments show that the effect subject of the reconstructed model is clear.

A New Method to Concentrate Electromagnetic Field Within ROI Using a High Dielectric Material in 3T Body MRI

2013 ◽  
Author(s):  
Bu S. Park ◽  
Sunder S. Rajan ◽  
Leonardo M. Angelone
Keyword(s):  
Numerical Simulation ◽  
Electromagnetic Field ◽  
Human Body ◽  
Region Of Interest ◽  
Dielectric Materials ◽  
The Body ◽  
Human Body Model ◽  
Body Model ◽  
Simulation Results ◽  
High Dielectric

We present numerical simulation results showing that high dielectric materials (HDMs) when placed between the human body model and the body coil significantly alter the electromagnetic field inside the body. The numerical simulation results show that the electromagnetic field (E, B, and SAR) within a region of interest (ROI) is concentrated (increased). In addition, the average electromagnetic fields decreased significantly outside the region of interest. The calculation results using a human body model and HDM of Barium Strontium Titanate (BST) show that the mean local SAR was decreased by about 56% (i.e., 18.7 vs. 8.2 W/kg) within the body model.

Sign in / Sign up

Export Citation Format

Share Document