Top-Down Approaches in Human Pose Estimation: A State-of-the Art Review

Yusuf Enes Bölükbaşı, Rayan Abri
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Abstract

This paper offers a comprehensive exploration of top-down approaches in human poseestimation, a key facet of computer vision. These approaches primarily focus on identifying the humansubject in an image or video, followed by determining the spatial configuration of their body joints. Suchtechniques are instrumental in an array of sectors, from healthcare and sports analytics to entertainment andsecurity systems.The document delves into the foundations of top-down pose estimation, presenting a review of establishedand emerging models. It explicates the role of key performance metrics, including Average Precision (AP),AP at specific Intersection over Union (IoU) thresholds (AP50, AP75), Average Recall (AR), and AR at anIoU of 0.50, in appraising the efficiency and reliability of these models.The paper underscores the substantial strides made in top-down pose estimation and discusses their efficacyin managing diverse real-world scenarios. It draws attention to the various challenges associated with thesetechniques, such as handling occlusions, processing images or videos with multiple individuals, andaddressing computational constraints.In conclusion, while top-down approaches in pose estimation have shown notable progress and promise,there exist avenues for further research and development. This paper intends to provide a foundationalunderstanding of these techniques and a platform for future advancements in the field.
自上而下的方法在人体姿态估计:一个国家的艺术评论
本文提供了一个全面的探索自顶向下的方法在人体姿态估计,计算机视觉的一个关键方面。这些方法主要集中在识别图像或视频中的人体主体,然后确定其身体关节的空间结构。这些技术在从医疗保健和体育分析到娱乐和安全系统的一系列领域都发挥着重要作用。该文件深入研究了自顶向下姿态估计的基础,对已建立的和新兴的模型进行了回顾。它阐明了关键绩效指标在评估这些模型的效率和可靠性方面的作用,包括平均精度(AP),特定交叉交叉(IoU)阈值的AP (AP50, AP75),平均召回率(AR)和anIoU为0.50时的AR。本文强调了自顶向下姿态估计取得的实质性进展,并讨论了它们在管理各种现实世界场景中的有效性。它引起了人们对与这些技术相关的各种挑战的关注,例如处理遮挡,处理多个个体的图像或视频,以及解决计算限制。综上所述,虽然自顶向下的姿态估计方法已经显示出显著的进步和前景,但仍有进一步研究和发展的途径。本文旨在提供对这些技术的基本理解,并为该领域的未来发展提供平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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