scholarly journals Dynamic Pose Estimation Using Multiple RGB-D Cameras

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3865 ◽  
Author(s):  
Sungjin Hong ◽  
Yejin Kim
Keyword(s):  
Pose Estimation ◽  
Extreme Points ◽  
Graph Model ◽  
Human Action ◽  
Body Parts ◽  
Physical Constraints ◽  
Depth Images ◽  
Motion Sequence ◽  
Human Pose ◽  
Hands And Feet

Human poses are difficult to estimate due to the complicated body structure and the self-occlusion problem. In this paper, we introduce a marker-less system for human pose estimation by detecting and tracking key body parts, namely the head, hands, and feet. Given color and depth images captured by multiple red, green, blue, and depth (RGB-D) cameras, our system constructs a graph model with segmented regions from each camera and detects the key body parts as a set of extreme points based on accumulative geodesic distances in the graph. During the search process, local detection using a supervised learning model is utilized to match local body features. A final set of extreme points is selected with a voting scheme and tracked with physical constraints from the unified data received from the multiple cameras. During the tracking process, a Kalman filter-based method is introduced to reduce positional noises and to recover from a failure of tracking extremes. Our system shows an average of 87% accuracy against the commercial system, which outperforms the previous multi-Kinects system, and can be applied to recognize a human action or to synthesize a motion sequence from a few key poses using a small set of extremes as input data.

scholarly journals Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier

Sensors ◽  
2015 ◽  
Vol 15 (6) ◽  
pp. 12410-12427 ◽  
Author(s):  
Hanguen Kim ◽  
Sangwon Lee ◽  
Dongsung Lee ◽  
Soonmin Choi ◽  
Jinsun Ju ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Human Pose Estimation ◽  
Svm Classifier ◽  
Depth Images ◽  
Human Pose

scholarly journals Mixing body‐parts model for 2D human pose estimation in stereo videos

2017 ◽  
Vol 11 (6) ◽  
pp. 426-433 ◽  
Author(s):  
Manuel I. López‐Quintero ◽  
Manuel J. Marín‐Jiménez ◽  
Rafael Muñoz‐Salinas ◽  
Rafael Medina‐Carnicer
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Body Parts ◽  
Human Pose

Parallel Deep Learning Ensembles for Human Pose Estimation

2018 ◽  
Author(s):  
Hailin Ren ◽  
Anil Kumar ◽  
Xinran Wang ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Pose Estimation ◽  
Parallel Architecture ◽  
Body Parts ◽  
Post Processing ◽  
Computationally Efficient ◽  
Human Pose ◽  
Learning Ensembles ◽  
The Individual ◽  
Computational Resources ◽  
Mutually Independent

This paper presents an efficient method to detect human pose with monocular color imagery using a parallel architecture based on deep neural network. The network presented in this approach consists of two sequentially connected stages of 13 parallel CNN ensembles, where each ensemble is trained to detect one specific kind of linkage of the human skeleton structure. After detecting all skeleton linkages, a voting score-based post-processing algorithm assembles the individual linkages to form a complete human structure. This algorithm exploits human structural heuristics while assembling skeleton links and searches only for adjacent link pairs around the expected common joint area. The use of structural heuristics in the presented approach heavily simplifies the post-processing computations. Furthermore, the parallel architecture of the presented network enables mutually independent computing nodes to be efficiently deployed on parallel computing devices such as GPUs for computationally efficient training. The proposed network has been trained and tested on the COCO 2017 person-keypoints dataset and delivers pose estimation performance matching state-of-art networks. The parallel ensembles architecture improves its adaptability in applications aimed at identifying only specific body parts while saving computational resources.

scholarly journals Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation

2019 ◽  
Vol 33 ◽  
pp. 898-905 ◽  
Author(s):  
Wei Feng ◽  
Wentao Liu ◽  
Tong Li ◽  
Jing Peng ◽  
Chen Qian ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Message Passing ◽  
Closed Loop ◽  
State Of The Art ◽  
Human Action ◽  
Complementary Information ◽  
Learning Framework ◽  
Human Object ◽  
Human Pose ◽  
Object Interactions

Human-object interactions (HOI) recognition and pose estimation are two closely related tasks. Human pose is an essential cue for recognizing actions and localizing the interacted objects. Meanwhile, human action and their interacted objects’ localizations provide guidance for pose estimation. In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously. First, two modules are designed to enforce message passing between the tasks, i.e. pose aware HOI recognition module and HOI guided pose estimation module. Then, these two modules form a closed loop to utilize the complementary information iteratively, which can be trained in an end-to-end manner. The proposed method achieves the state-of-the-art performance on two public benchmarks including Verbs in COCO (V-COCO) and HICO-DET datasets.

scholarly journals Latent variable pictorial structure for human pose estimation on depth images

2016 ◽  
Vol 203 ◽  
pp. 52-61 ◽  
Author(s):  
Li He ◽  
Guijin Wang ◽  
Qingmin Liao ◽  
Jing-Hao Xue
Keyword(s):  
Pose Estimation ◽  
Latent Variable ◽  
Human Pose Estimation ◽  
Depth Images ◽  
Human Pose ◽  
Pictorial Structure

scholarly journals An Evaluation of Pose Estimation in Video of Traditional Martial Arts Presentation

2019 ◽  
Vol 2019 (2) ◽  
pp. 114-126
Author(s):  
Nguyễn Tường Thành ◽  
Lê Văn Hùng ◽  
Phạm Thành Công
Keyword(s):  
National Culture ◽  
Pose Estimation ◽  
Martial Arts ◽  
Social Life ◽  
Body Parts ◽  
Deviation Angle ◽  
Self Defense ◽  
Rgb Images ◽  
Human Pose ◽  
Small Dataset

Preserving, maintaining, and teaching traditional martial arts are very important activities in social life. That helps individuals preserve national culture, exercise, and practice self-defense. However, traditional martial arts have many differentposturesaswellasvariedmovementsofthebodyand body parts. The problem of estimating the actions of human body still has many challenges, such as accuracy, obscurity, and so forth. This paper begins with a review of several methods of 2-D human pose estimation on the RGB images, in which the methods of using the Convolutional Neural Network (CNN) models have outstanding advantages in terms of processing time and accuracy. In this work we built a small dataset and used CNN for estimating keypoints and joints of actions in traditional martial arts videos. Next we applied the measurements (length of joints, deviation angle of joints, and deviation of keypoints) for evaluating pose estimation in 2-D and 3-D spaces. The estimator was trained on the classic MSCOCO Keypoints Challenge dataset, the results were evaluated on a well-known dataset of Martial Arts, Dancing, and Sports dataset. The results were quantitatively evaluated and reported in this paper.

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