Human Pose Estimation from Depth Image Using Visibility Estimation and Key Points

The goal of this Chapter is to introduce an efficient and standard approach for human pose estimation. This approach is based on a bottom up parsing technique which uses a non-parametric representation known as Greedy Part Association Vector (GPAVs), generates features for localizing anatomical key points for individuals. Taking leaf out of existing state of the art algorithm, this proposed algorithm aims to estimate human pose in real time and optimize its results. This approach simultaneously detects the key points on human body and associates them by learning the global context. However, In order to operate this in real environment where noise is prevalent, systematic sensors error and temporarily crowded public could pose a challenge, an efficient and robust recognition would be crucial. The proposed architecture involves a greedy bottom up parsing that maintains high accuracy while achieving real time performance irrespective of the number of people in the image.

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GYM REP TRACKER USING MEDIAPIPE AND PYTHON

International journal of multidisciplinary advanced scientific research and innovation ◽

10.53633/ijmasri.2021.1.10.001 ◽

2021 ◽

Vol 1 (10) ◽

pp. 240-245

Author(s):

Rahul Ratusaria ◽

Tushar Baghel ◽

Ayush Chander Vanshi ◽

Neeraj Garg

Keyword(s):

Real Time ◽

Pose Estimation ◽

Human Pose Estimation ◽

Suitable Model ◽

The Past ◽

Key Points ◽

Intelligence Models ◽

Artificial Intelligence Models ◽

Human Pose ◽

Computational Resources

Human Pose estimation has grabbed the eye of the computer vision community for the past few decades. It is a vital step closer to knowledge people in pics and motion pictures. Strong articulations, small and hardly visible joints, occlusions, apparel, and lighting changes make it very difficult to perform estimate pose. Human Pose estimation is an important problem that needed to be study. It is used to detect human anatomical key points (e.g., shoulder, elbows, legs, wrist, etc.) in real time using less computational resources. There are many Artificial Intelligence models i.e, Posenet, OpenPose1 and MediaPipe8 for Real time Human Pose Estimation. Many experiments has performed to find out the best suitable model for Human Pose Estimation. Experiments stated that PoseNet is suitable to run on lightweight devices like browsers whereas OpenPose meant to run on GPU powered devices and is more accurate. On the other hand, MediaPipe is very fast, modular, reusable and highly efficient. Hence, our model uses the MediaPipe to perform its estimation. Keywords: Pose estimation, Gym Rep Tracker, Media Pipe, Python, Machine learning

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Deep neural networks for human pose estimation from a very low resolution depth image

Multimedia Tools and Applications ◽

10.1007/s11042-019-7433-7 ◽

2019 ◽

Vol 78 (20) ◽

pp. 29357-29377 ◽

Cited By ~ 5

Author(s):

Piotr Szczuko

Keyword(s):

Neural Networks ◽

Pose Estimation ◽

Deep Neural Networks ◽

Depth Image ◽

Human Pose Estimation ◽

Low Resolution ◽

Human Pose

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Abnormal Driving Detection Based on Human Pose Estimation and Facial Key Points Detection

Intelligent Computing Theories and Application - Lecture Notes in Computer Science ◽

10.1007/978-3-030-84529-2_8 ◽

2021 ◽

pp. 91-102

Author(s):

Zihao Ye ◽

Qize Wu ◽

Xinxin Zhao ◽

Jiajun Zhang ◽

Wei Yu ◽

...

Keyword(s):

Pose Estimation ◽

Human Pose Estimation ◽

Key Points ◽

Human Pose

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The Aircraft Pose Estimation Based on a Convolutional Neural Network

Mathematical Problems in Engineering ◽

10.1155/2019/7389652 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Daoyong Fu ◽

Wei Li ◽

Songchen Han ◽

Xinyan Zhang ◽

Zhaohuan Zhan ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Pose Estimation ◽

Estimation Method ◽

Human Pose Estimation ◽

Two Dimensional ◽

Surveillance Video ◽

Key Points ◽

Human Pose

The pose estimation of the aircraft in the airport plays an important role in preventing collisions and constructing the real-time scene of the airport. However, current airport target surveillance methods regard the aircraft as a point, neglecting the importance of pose estimation. Inspired by human pose estimation, this paper presents an aircraft pose estimation method based on a convolutional neural network through reconstructing the two-dimensional skeleton of an aircraft. Firstly, the key points of an aircraft and the matching relationship are defined to design a 2D skeleton of an aircraft. Secondly, a convolutional neural network is designed to predict all key points and components of the aircraft kept in the confidence maps and the Correlation Fields, respectively. Thirdly, all key points are coarsely matched based on the matching relationship and then refined through the Correlation Fields. Finally, the 2D skeleton of an aircraft is reconstructed. To overcome the lack of benchmark dataset, the airport surveillance video and Autodesk 3ds Max are utilized to build two datasets. Experiment results show that the proposed method get better performance in terms of accuracy and efficiency compared with other related methods.

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