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基于骨的混合动力模板,用于交叉视图和外观强大的步态识别
João Ferreira Nunes1, Pedro Miguel Moreira2, João Manuel R S Tavares3
1Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Viana do Castelo, Avenida do Atlântico, 4900-348 Viana do Castelo, Portugal.
Journal of imaging
|January 27, 2026
概括
新的步态骨图像 (GSI) 通过将3D运动编码为紧的2D模板,提供了强大的人类识别. 与传统方法相比,这些骨描述符在不同的视图和外观上显示出更好的稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 生物识别信息 生物识别信息
- 人类运动分析分析
背景情况:
- 基于轮的步态识别 (例如,步态能量图像) 在受控设置中表现出色,但在外观变化方面失败.
- 基于骨架的方法提供了可解释性,但很容易产生定位估计的不准确性.
研究的目的:
- 引入新的紧的2D骨描述器,步行骨图像 (GSIs),以增强步行识别.
- 开发一个统一的管道,用于将3D关节轨迹处理成与2D CNNs兼容的静态模板.
- 评估GSI的稳定性和绩效与外观和观点的变化.
主要方法:
- 将3D关节轨迹编码为基于线的和基于关节的2D骨架模板 (GSI).
- 实现一个处理管道,包括拓规范化,视图对齐,投影和染.
- 分析GRIDDS数据集的核心设计因素,评估CASIA-B数据集的性能.
主要成果:
- 与步行能量图像相比,GSI变体在不同的外观条件下 (视角,服装,携带物体) 显示出较低的性能退化.
- 在严重的交叉视图步态识别场景下,GSI表现出更大的稳定性.
- 虽然步行能量图像实现了更高的相同视图准确性,但GSI提供了更高的稳定性.
结论:
- 像GSI这样的紧骨模板可以有效地补充基于外观的步态识别方法.
- 拟议的步行骨图像显示了改善人类识别系统稳健性的承诺.
- 未来3D人体姿势估计方面的进步可能会进一步提高基于骨模板的步态识别的实用性.
关键词:
卡西亚-B是什么意思网格 网格 网格 网格步态骨图像 (GSI) 的一个例子.外观的变化,外表的变化同变的强度强度.步态识别系统可以识别步态.人类姿势估计估计动作模板运动模板基于骨架的表示形式.视角的变化 观点的变化更多相关视频
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