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The Real-Time Depth Estimation for an Occluded Person Based on a Single Image and OpenPose Method
Mathematics ( IF 2.4 ) Pub Date : 2020-08-10 , DOI: 10.3390/math8081333
Yu-Shiuan Tsai , Li-Heng Hsu , Yi-Zeng Hsieh , Shih-Syun Lin

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.

中文翻译:

基于单张图像和OpenPose方法的被遮挡人实时深度估计

近年来,神经网络的突破和深度学习的兴起导致了机器视觉的发展,机器视觉已在图像识别的实际应用中得到了广泛应用。汽车,无人机,便携式设备,行为识别,室内定位和许多其他行业也依赖于集成应用程序,并且需要深度学习和机器视觉的支持。对于这些技术,对与肖像或物体的识别有关的精度有很高的要求。人物形象的识别也是一个研究目标,引起了各个领域的高度关注。但是,肖像会受到各种因素的影响,例如身高,体重,姿势,角度以及是否被遮盖,这会影响识别的准确性。本文将深度学习的应用应用于具有不同姿势和角度的肖像,尤其是用于阴影肖像的单镜头的实际距离(深度估计),以便将来可用于无人机的自动控制。传统的使用图像计算深度的方法主要分为三种:一种是单镜头估计,两种是镜头估计,以及三种是光学波段估计。鉴于第二和第三类别都需要相对较大且昂贵的设备来有效地执行距离计算,因此最近产生了许多使用单个透镜来计算距离的方法。但是,是否使用传统的“距离测量校准单位”,“散焦距离测量”,或“三维网格空间消息距离测量方法”,所有这些都面临相应的困难和问题。另外,他们必须处理外部干扰并处理阴影图像。因此,在卡内基梅隆大学提出的新研究方法OpenPose下,本文旨在提出一种用于单镜头遮挡人像的深度算法,以估计不同姿势,视角和遮挡的实际人像距离。
更新日期:2020-08-10
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