The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method
In recent years, the breakthrough of neural networks and the rise of deep learning have led to the advancement of machine vision, which has been commonly used in the practical application of image recognition. Automobiles, drones, portable devices, behavior recognition, indoor positioning and many o...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/8/8/1333 |
| _version_ | 1797559073231601664 |
|---|---|
| author | Yu-Shiuan Tsai Li-Heng Hsu Yi-Zeng Hsieh Shih-Syun Lin |
| author_facet | Yu-Shiuan Tsai Li-Heng Hsu Yi-Zeng Hsieh Shih-Syun Lin |
| author_sort | Yu-Shiuan Tsai |
| collection | DOAJ |
| description | In recent years, the breakthrough of neural networks and the rise of deep learning have led to the advancement of machine vision, which has been commonly used in the practical application of image recognition. Automobiles, drones, portable devices, behavior recognition, indoor positioning and many other industries also rely on the integrated application, and require the support of deep learning and machine vision. As for these technologies, there is a high demand for the accuracy related to the recognition of portraits or objects. The recognition of human figures is also a research goal that has drawn great attention in various fields. However, the portrait will be affected by various factors such as height, weight, posture, angle and whether it is covered or not, which affects the accuracy of recognition. This paper applies the application of deep learning to portraits with different poses and angles, especially the actual distance of a single lens for the shadowed portrait (depth estimation), so that it can be used for automatic control of drones in the future. Traditional methods for calculating depth using images are mainly divided into three types: one—single-lens estimation, two—lens estimation, and three—optical band estimation. In view of the fact that both the second and third categories require relatively large and expensive equipment to effectively perform distance calculations, numerous methods for calculating distance using a single lens have recently been produced. However, whether it is the use of traditional “units of distance measurement calibration”, “defocus distance measurement”, or the “three-dimensional grid space messages distance measurement method”, all of these face corresponding difficulties and problems. Additionally, they have to deal with outside disturbances and process the shadowed image. Therefore, under the new research method, OpenPose, which is proposed by Carnegie Mellon University, this paper intends to propose a depth algorithm for a single-lens occluded portrait to estimate the actual portrait distance for different poses, angles of view and obscuration. |
| first_indexed | 2024-03-10T17:40:32Z |
| format | Article |
| id | doaj.art-23a1a0dd610c407789cf37faf313b7d4 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T17:40:32Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-23a1a0dd610c407789cf37faf313b7d42023-11-20T09:42:46ZengMDPI AGMathematics2227-73902020-08-0188133310.3390/math8081333The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose MethodYu-Shiuan Tsai0Li-Heng Hsu1Yi-Zeng Hsieh2Shih-Syun Lin3Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, TaiwanDepartment of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, TaiwanDepartment of Electrical Engineering, National Taiwan Ocean University, Keelung City 202, TaiwanDepartment of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, TaiwanIn recent years, the breakthrough of neural networks and the rise of deep learning have led to the advancement of machine vision, which has been commonly used in the practical application of image recognition. Automobiles, drones, portable devices, behavior recognition, indoor positioning and many other industries also rely on the integrated application, and require the support of deep learning and machine vision. As for these technologies, there is a high demand for the accuracy related to the recognition of portraits or objects. The recognition of human figures is also a research goal that has drawn great attention in various fields. However, the portrait will be affected by various factors such as height, weight, posture, angle and whether it is covered or not, which affects the accuracy of recognition. This paper applies the application of deep learning to portraits with different poses and angles, especially the actual distance of a single lens for the shadowed portrait (depth estimation), so that it can be used for automatic control of drones in the future. Traditional methods for calculating depth using images are mainly divided into three types: one—single-lens estimation, two—lens estimation, and three—optical band estimation. In view of the fact that both the second and third categories require relatively large and expensive equipment to effectively perform distance calculations, numerous methods for calculating distance using a single lens have recently been produced. However, whether it is the use of traditional “units of distance measurement calibration”, “defocus distance measurement”, or the “three-dimensional grid space messages distance measurement method”, all of these face corresponding difficulties and problems. Additionally, they have to deal with outside disturbances and process the shadowed image. Therefore, under the new research method, OpenPose, which is proposed by Carnegie Mellon University, this paper intends to propose a depth algorithm for a single-lens occluded portrait to estimate the actual portrait distance for different poses, angles of view and obscuration.https://www.mdpi.com/2227-7390/8/8/1333depth estimationopenposeoccluded person |
| spellingShingle | Yu-Shiuan Tsai Li-Heng Hsu Yi-Zeng Hsieh Shih-Syun Lin The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method Mathematics depth estimation openpose occluded person |
| title | The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method |
| title_full | The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method |
| title_fullStr | The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method |
| title_full_unstemmed | The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method |
| title_short | The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method |
| title_sort | real time depth estimation for an occluded person based on a single image and openpose method |
| topic | depth estimation openpose occluded person |
| url | https://www.mdpi.com/2227-7390/8/8/1333 |
| work_keys_str_mv | AT yushiuantsai therealtimedepthestimationforanoccludedpersonbasedonasingleimageandopenposemethod AT lihenghsu therealtimedepthestimationforanoccludedpersonbasedonasingleimageandopenposemethod AT yizenghsieh therealtimedepthestimationforanoccludedpersonbasedonasingleimageandopenposemethod AT shihsyunlin therealtimedepthestimationforanoccludedpersonbasedonasingleimageandopenposemethod AT yushiuantsai realtimedepthestimationforanoccludedpersonbasedonasingleimageandopenposemethod AT lihenghsu realtimedepthestimationforanoccludedpersonbasedonasingleimageandopenposemethod AT yizenghsieh realtimedepthestimationforanoccludedpersonbasedonasingleimageandopenposemethod AT shihsyunlin realtimedepthestimationforanoccludedpersonbasedonasingleimageandopenposemethod |