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骨的深度融合为行动识别提供空间-时间和动态信息

Song Gao¹, Dingzhuo Zhang², Zhaoming Tang¹

¹Aviation Maintenance NCO Academy, Air Force Engineering University, Xinyang 464007, China.

Sensors (Basel, Switzerland)

|December 17, 2024

概括

这项研究引入了一种新的动作识别方法,使用骨架的时空和动态特征与双流卷积神经网络 (TS-CNN). 该方法在复杂环境中显著提高了人类行动识别准确度.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器学习机器学习

背景情况:

对于动作识别的传统深度学习方法的识别率很低.
现有的算法往往无法有效地捕捉复杂的人类运动.

研究的目的:

开发一个改进的动作识别算法,使用骨架的时空和动态特征.
在复杂的环境中提高人类行动识别的准确性和稳定性.

主要方法:

骨架数据被转换为提取相对关节位置,并为时空特征编码为颜色纹理图.
人体的约束被用来改善阶级间的区别.
联合速度信息被编码为动态特征的颜色纹理图.
使用运动突出度和形态运算符来增强特征.
一个双流卷积神经网络 (TS-CNN) 用于深度融合和动作识别.

主要成果:

开发的方法实现了高的认可率:NTU RGB-D的86.25%,西北UCLA的87.37%和UTD-MHAD的93.75%.
与最先进的算法相比,该方法显示出更高的性能.

结论:

提出的基于骨架的动作识别方法有效提高了准确性.
时空,动态特征和TS-CNN的结合为复杂的人类行动识别提供了强大的解决方案.

关键词:

行动的认可行动的认可功能增强功能增强功能增强骨架动态特征骨架动态特征骨架空间时间特征两个流的卷积神经网络.

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