从自我中心的角度来看,外观和运动特征的融合,用于从自我中心的角度识别日常活动
Mohd Haris Lye1, Nouar AlDahoul1,2, Hezerul Abdul Karim1
1Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新的方法,用于从可穿戴相机视频中识别日常生活活动. 该方法结合了视觉和运动特征,在基准数据集上实现了高精度.
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:
- 穿戴式摄像机的自我中心视频为识别日常生活活动提供了一致的观点.
- 穿戴式传感器的活动识别面临着诸如运动模糊和显著变化的挑战.
- 当前的方法依赖于依赖于域的手工制作的特征,限制了它们的概括性.
研究的目的:
- 提出一种新的解决方案,以自我中心的角度从预先分割的视频剪辑中识别日常生活活动.
- 开发一种有效结合外观和运动特征的方法,以改善活动识别.
- 在公开的基准数据集上评估拟议解决方案的性能.
主要方法:
- 使用预训练的卷积神经网络 (CNN) 模型VGG16,从视频中提取视觉特征.
- 开发了新的聚合方案:平均和最大空间聚合 (MMSP) 和最大平均时间金字塔 (TPMM) 聚合以聚合特征.
- 视频的外观特征与光流图像的运动特征相结合.
- 采用线性支持向量机 (SVM) 分类器进行活动识别.
主要成果:
- 提出的解决方案在识别日常生活活动方面显示出有希望的结果.
- 外观和运动特征的聚合被证明是活动识别的优势.
- 实现了高分类准确率:LENA为90.38%,ADL为75.37%,FPPA为96.08%.
- 获得的竞争性平均每类精度 (AP):58.42% (ADL) 和96.11% (FPPA).
结论:
- 拟议的MMSP-TPMM方法与公开数据集上的现有方法相比,显示出更高的性能.
- 结合外观和运动特征的方法对于自我中心的日常活动识别是有效的.
- 开发的解决方案具有在活动识别系统中的实际应用的巨大潜力.
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