角度信息有助于基于骨架的行动识别
Chengming Liu1, Jiahao Guan1, Haibo Pang1
1School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, Henan, China.
这项研究通过引入共弦流和关键采样来增强基于人类骨的动作识别,用于像花样滑冰这样的复杂活动. 这些方法在不改变核心网络的情况下提高了准确性,提高了对具有挑战性的数据集的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人类行动识别 人类行动识别
背景情况:
- 图形卷积网络 (GCNs) 在基于骨架的动作识别方面表现出色,但由于缺乏不变性和依赖可变运动数据,因此在复杂的动作方面陷入困境.
- 现有的方法与视角扭曲和个体运动变异性作斗争,阻碍了像艺术滑冰识别等任务的性能.
研究的目的:
- 为了提高基于骨架的行动识别对复杂的人类活动的稳定性和准确性.
- 解决图形卷积网络 (GCNs) 在处理尺度,旋转和运动可变性的局限性.
- 引入新的空间和时间特征提取方法,而不改变GCN骨干.
主要方法:
- 开发了一种新的等号流,以增强空间特征的强度,防止扭曲.
- 引入了一个Keyframe采样算法,用于高效和有效的时间特征提取,绕过运动信息.
- 拟议的方法被整合,而不需要对现有的GCN骨干架构进行修改.
主要成果:
- 与最先进的方法相比,在FSD-10艺术滑冰数据集上的Top-1准确度提高了2.6%.
- 在FineGYM和NTU RGB+D数据集上表现出卓越的性能,验证了该方法的通用性.
- 关键框架采样算法有效提取时间特征,减少对潜在误导性的运动数据的依赖.
结论:
- 拟议的共弦值流和关键框架采样显著提高了基于骨架的复杂动作的动作识别.
- 该方法提供了更高的准确性和稳定性,特别是在运动变化很高的活动中,如花样滑冰.
- 这种方法为人类行动识别提供了宝贵的进步,在不进行建筑变更的情况下提供更好的性能.
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