基于双流模式的深度学习方法,用于在视频中增强双人人际交互识别
Hemel Sharker Akash1, Md Abdur Rahim1, Abu Saleh Musa Miah2
1Department of Computer Science and Engineering, Pabna University of Science and Technology, Rajapur 6600, Bangladesh.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
这项研究引入了一种新的双流深度学习系统,用于视频中的人际交互识别 (HIR). 拟议的模型实现了高精度,在基准数据集上表现优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 模式识别 模式识别
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 人与人互动识别 (HIR) 对于医疗保健和监控等应用至关重要.
- 现有的基于视频的HIR系统难以处理复杂的操作,不同的观点和环境因素.
- 挑战包括由于运动变化和数据复杂性而实现令人满意的性能.
研究的目的:
- 提出一个强大的基于深度学习的双流HIR系统.
- 提高视频中人类互动识别的准确性和可靠性.
- 为了解决当前HIR方法的局限性.
主要方法:
- 一个双流深度学习架构,利用骨架和RGB数据.
- 流1:YOLOv8-用于姿势提取的姿势,LSM模块和密集层.
- 流2:分割任何模型 (SAM) 用于网格生成,LSTM/GRU用于远程依赖,以及自定义过.
- 通过连接的特征融合,然后是分类模块.
主要成果:
- 在两个基准数据集上实现了96.56%和96.16%的准确性.
- 与现有的人类互动识别模型相比,表现出更高的性能.
- 定制波器功能通过删除无关数据来提高计算效率.
结论:
- 拟议的双流深度学习系统显著提升了人类互动识别.
- 该模型的高精度验证了它的有效性和优越性.
- 这种方法为复杂的基于视频的交互分析提供了可靠的解决方案.
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