一个基于MGSE和TAAC的多维特征融合网络,用于基于视频的人类行动识别
Shuang Zhou1, Hongji Xu1, Zhiquan Bai1
1School of Information Science and Engineering, Shandong University, 72 Binhai Road, Qingdao, 266237, Shandong, China.
概括
本研究介绍了P-MTSC3D,这是一种用于人类行动识别 (HAR) 的新型网络,可以有效地提取抽象特征. 它在基准数据集上达到很高的准确性,并且在囚犯监控等专业应用中表现有前途.
科学领域:
- 计算机视觉和人工智能的人工智能
- 机器学习用于人类行动识别 (HAR)
背景情况:
- 目前基于视频的HAR方法在抽象特征提取方面遇到了困难.
- 对于特殊群体,如囚犯和老年人,行动识别能力有限.
- 现有的HAR技术缺乏对复杂的人类行为进行全面的特征提取.
研究的目的:
- 提出一种新的多维特征融合网络,P-MTSC3D,用于增强人类行动识别.
- 为解决特殊人员抽象特征提取和识别方面的局限性.
- 提高HAR在各种现实场景中的准确性和适用性.
主要方法:
- 开发了一个并行网络 (P-MTSC3D),集成上下文建模和时间适应性注意力.
- 采用了三分支架构:基本特征提取,空间通道特征提取 (MGSE模块) 和时间特征提取 (TAAC单元).
- 在UCF101和HMDB51数据集上验证了网络,并引入了一个新的监狱行动 (PA) 数据集.
主要成果:
- 实现了高识别精度:UCF101上的97.92%和HMDB51上的75.59%.
- 与最先进的HAR网络相比,其表现优越.
- 通过对新建的PA数据集的实验,在专业场景中展示了有效性.
结论:
- P-MTSC3D网络有效地提取多维特征,以准确识别人类行动.
- 拟议的方法比现有的HAR技术提供了显著的改进,特别是在复杂的行动和特定群体.
- P-MTSC3D显示出强大的现实应用潜力,包括对特殊人员的监视和监控.
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