基于KNN的机器学习分类器用于深度学习的空间运动特征,用于人类行动识别.
Kalaivani Paramasivam1, Mohamed Mansoor Roomi Sindha2, Sathya Bama Balakrishnan2
1Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakanur 625582, Tamilnadu, India.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
一个新的轻量级网络,HARNet,改善了监视中的人类行动识别. 这种方法使用了一种新的空间运动数据管道,性能优于当前最先进的方法.
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:
- 人类行动识别 (HAR) 对于监控视频分析至关重要.
- 现有的HAR方法通常依赖于计算密集的3D卷积神经网络 (CNN) 或双流网络.
- 由于大量的参数,这些重型网络在实施和培训方面存在挑战.
研究的目的:
- 开发一个计算效率高的HAR方法.
- 为了介绍HARNet,一个轻量级的定向环形图基于剩余的2D CNN.
- 创建一个新的管道,用于生成空间运动数据,用于动作表示学习.
主要方法:
- 从头开始设计了HARNet,一个定制的轻量级定向的基于循环图的剩余2D CNN.
- 从原始视频输入中构建空间运动数据的新管道.
- 将构建的输入输入到HARNet中,以便在单一流中同时处理空间和运动信息.
- 提取了用于使用传统机器学习模型进行分类的潜在表示.
主要成果:
- 与现有的最先进的方法相比,HARNet表现出更高的性能.
- 在UCF101数据集上实现了2.75%的百分比改进.
- 在HMDB51数据集上显示了10.94%的改进,在KTH数据集上显示了0.18%的改进.
结论:
- 哈恩特为人类行动识别提供了有效和高效的解决方案.
- 拟议的空间运动数据管道增强了对行动的潜在表示学习.
- 哈恩特的轻量级设计减轻了与沉重的深度学习模型相关的实施和培训挑战.
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