一种深度回归方法,用于在部分封闭下识别人类活动
Ioannis Vernikos1, Evaggelos Spyrou1, Ioannis-Aris Kostis1
1Department of Informatics and Telecommunications, University of Thessaly, 3rd Km Old National Road Lamia-Athens, Lamia 35132, Greece.
International journal of neural systems
|August 21, 2023
概括
这项研究引入了人类活动识别 (HAR) 的新型深度学习方法,该方法可以重建隐蔽的身体部位. 这种方法在现实场景中显著改善了HAR的性能,其中包括部分闭塞.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 人类活动识别 (HAR) 对于视频分析至关重要,但与被遮住的身体部位扎.
- 现有的HAR研究通常使用理想的,无遮蔽的数据集,限制了现实世界的适用性.
- 阻塞通过掩盖关键的身体运动,显著降低了HAR的性能.
研究的目的:
- 开发一种强大的HAR方法,能够处理部分身体部位的阻塞.
- 为了解决目前的哈尔研究中对闭塞挑战的低估问题.
- 在现实的,不受约束的环境中提高HAR准确性.
主要方法:
- 为HAR提出了一个新的深度卷积循环神经网络 (CRNN).
- 使用3D骨关节建模人类运动,假设封闭的部位保持封闭.
- 通过制定 HAR 作为回归任务来重建缺失的运动数据来解决阻塞.
主要成果:
- 在被封闭的数据上,与基线方法相比,实现了显著的性能提高.
- 证明了CRNN在重建塞的身体部位运动中的有效性.
- 在四个公开可用的人类运动数据集上验证了该方法.
结论:
- 提出的基于回归的CRNN有效地处理HAR中的部分阻塞.
- 这项工作是第一个将HAR在阻塞下作为回归问题的研究.
- 这些发现为在现实世界应用中更可靠的HAR系统铺平了道路.
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