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ROM-Pose:恢复被遮住的面具图像,用于2D人类姿势估计

Yunju Lee¹, Jihie Kim²

¹Department of Artificial Intelligence, Dongguk University, Seoul, Republic of South Korea.

PeerJ. Computer science

|June 26, 2025

概括

这项研究介绍了ROM-Pose,这是一种通过恢复隐蔽的图像部分来改善人类姿势估计 (HPE) 的新方法. 这提高了界限框的准确性,以便更精确地检测关键点.

科学领域:

计算机视觉计算机视觉
机器学习机器学习

背景情况:

人体姿势估计 (HPE) 对于理解图像中的人类行为至关重要.
上下HPE方法依赖于准确的界限框检测,这是受到遮的挑战.
隐蔽的身体部位往往被排除在HPE模型输入之外,从而降低了准确性.

研究的目的:

为了解决因遮导致的界限框检测失败引起的上下人类姿势估计中的不准确性.
引入一种新的方法,ROM-Pose,可以恢复封闭的图像区域以改善HPE.
通过包括以前封闭区域的信息来增强HPE模型的输入.

主要方法:

开发了ROM-Pose,该系统结合了恢复模型和HPE模型.
恢复模型使用一个专门的全常见对象在上下文 (COCO) 数据集来识别和恢复封闭的图像部分.
恢复的图像与原始RGB图像集成,为HPE模型提供完整的输入.

主要成果:

ROM-Pose成功地恢复了被遮住的图像部分,从而实现了更全面的界限框检测.
增强的输入使HPE模型能够识别以前被排除的身体部位.
与基线HPE方法相比,平均精度 (AP) 提高了1.6%.

结论:

ROM-Pose有效地减轻了遮对人类姿势估计准确性的负面影响.
恢复封闭的图像信息是一种可行的策略,可以改善HPE中的界限框检测.
拟议的方法在复杂的视觉场景中为强大的人类姿势估计提供了重大进步.

关键词:

阿莫达尔实例细分的细分方法估计估计估计人类姿势估计估计恢复恢复恢复恢复分段化分段化分段化分段化

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