视听多模式驱动的混合功能学习模型用于人群分析和分类
1Department of Electronics and Communication Engineering, Malnad College of Engineering, Visvesvaraya Technological University, Belagavi, India.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
这项研究引入了一种用于人群分析的新型视听模型,在具有挑战性的条件下提高了准确性. 混合方法结合了视觉和声学特征,可靠地进行人群分类和实时监控.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 基于视觉的群众分析与复杂的特征和极端条件作斗争,导致准确性低.
- 现有的方法缺乏声学线索,导致人群分类的模糊性.
- 整合视听数据为更可靠的人群分析提供了途径.
研究的目的:
- 为群众分析和分类开发一种新的视听多模式混合特征学习模型.
- 提高人群分析的准确性和可靠性,特别是在具有挑战性的环境条件下.
- 通过结合声学信息来解决仅视觉方法的局限性.
主要方法:
- 混合特征提取使用灰级共发生指标 (GLCM) 和AlexNet用于深度时空视觉特征.
- 声学特征提取包括GTCC,MFCC,光谱,光谱流,光谱斜率,和和声与噪声比 (HNR).
- 音视频特征的融合,然后使用随机森林组合分类器进行分类.
主要成果:
- 实现了98.26%的多类人群分类准确度.
- 报告的高性能指标:精度 (98.89%),灵敏度 (94.82%),特异性 (95.57%) 和F-测量 (98.84%).
- 证明了对现实世界的群众检测和分类任务的稳定性和适用性.
结论:
- 拟议的视听多模式模型显著提高了人群分析的准确性和可靠性.
- 混合特征学习方法有效地克服了基于视觉的系统的局限性,特别是在极端条件下.
- 该模型的稳定性证实了其在监视和监控中的实际应用潜力.
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