The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method

Yu-Shiuan Tsai, Li-Heng Hsu, Yi-Zeng Hsieh and Shih-Syun Lin
Additional contact information
Yu-Shiuan Tsai: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan
Li-Heng Hsu: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan
Yi-Zeng Hsieh: Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan
Shih-Syun Lin: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan

Mathematics, 2020, vol. 8, issue 8, 1-20

Abstract: 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.

Keywords: depth estimation; openpose; occluded person (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/8/1333/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/8/1333/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:8:p:1333-:d:397126

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().