视觉传感器用于通过混合功能和多类支持矢量机器自动识别人类活动
Saleha Kamal1, Haifa F Alhasson2, Mohammed Alnusayri3
1Department of Computer Science, Air University, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了一个新的人类活动识别 (HAR) 系统,使用时空特征和多类支持矢量机器. 该方法在识别各种数据集中的人类活动方面实现了高准确性.
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:
- 人类活动自动识别 (HAR) 对于监控和医疗保健等应用至关重要.
- 现有的HAR系统面临诸多挑战,包括照明不佳,视角不同,复杂的服装,类似的手势和有限的数据集.
研究的目的:
- 开发一个强大的HAR系统,能够准确地识别不同环境中的人类活动.
- 通过有效的特征提取和先进的机器学习模型来解决当前HAR系统的局限性.
主要方法:
- 为不同的活动属性提取独特的关键身体点和全身特征.
- 利用有效的时空特征来识别人类活动.
- 采用多类支向量机来提高分类性能.
主要成果:
- 拟议的系统在对基准数据集的分类,准确性和概括性方面表现出卓越的表现.
- 在BIT-Interaction上实现了88.61%的认可率,在UT-Interaction上达到87.33%,在NTU RGB + D 120上达到86.5%,在PKUMMD上达到81.25%.
结论:
- 开发的HAR系统通过利用时空特征和多类支持矢量机器有效地识别人类活动.
- 该模型在多个数据集中的高性能验证了其在现实场景中的效率和稳定性.
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