用世界杯优化和转移学习方法识别人类行动以了解场景
Ranjini Surendran1, Anitha J1, Jude D Hemanth1
1Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India.
PeerJ. Computer science
|June 22, 2023
概括
这项研究开发了一种用于识别视觉场景中人类活动的新方法. 将深度学习模型与自适应世界杯优化 (WCO) 结合起来,在分类步行和园艺等活动中实现了94.7%的准确性.
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:
- 人类活动识别对于视觉场景理解至关重要.
- 应用范围包括监控,医疗保健和娱乐.
- 复杂的人类活动涉及复杂的物体相互作用.
研究的目的:
- 在视觉场景中对各种人类活动进行分类.
- 提高人类活动识别模型的准确性.
- 通过优化功能选择来研究深度学习的有效性.
主要方法:
- 使用预先训练的深度卷积神经网络 (CNN),如AlexNet,SqueezeNet,ResNet和DenseNet进行特征提取.
- 采用了自适应世界杯优化 (WCO) 算法,用于优越的主导特征选择.
- 使用DenseNet 201完全连接的分类器进行分类的特征.
- 预处理的数据集与模糊的颜色堆叠.
主要成果:
- 使用DenseNet进行特征提取和WCO进行选择,实现了94.7%的分类准确度.
- 与没有特征选择的模型相比,表现出卓越的性能.
- 通过WCO进行双过的拟议方法改善了分类模型的质量.
结论:
- 深度CNN与基于WCO的特征选择的集成显著提高了人类活动的识别.
- 适应性WCO算法有效地识别了主导特征,提高了分类准确性.
- 这种方法为视觉场景理解和活动分类提供了强大的解决方案.
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