Analyzing the Subspaces Obtained by Dimensionality Reduction for Human Action Recognition from 3d Data

Marco Körner, Joachim Denzler
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引用次数: 6

Abstract

Since depth measuring devices for real-world scenarios became available in the recent past, the use of 3d data now comes more in focus of human action recognition. Due to the increased amount of data it seems to be advisable to model the trajectory of every landmark in the context of all other landmarks which is commonly done by dimensionality reduction techniques like PCA. In this paper we present an approach to directly use the subspaces (i.e. their basis vectors) for extracting features and classification of actions instead of projecting the landmark data themselves. This yields a fixed-length description of action sequences disregarding the number of provided frames. We give a comparison of various global techniques for dimensionality reduction and analyze their suitability for our proposed scheme. Experiments performed on the CMU Motion Capture dataset show promising recognition rates as well as robustness in the presence of noise and incorrect detection of landmarks.
基于降维的人体动作识别子空间分析
由于现实场景的深度测量设备在最近才出现,3d数据的使用现在更多地成为人类行为识别的焦点。由于数据量的增加,在所有其他地标的背景下对每个地标的轨迹进行建模似乎是明智的,这通常是通过PCA等降维技术来完成的。在本文中,我们提出了一种直接使用子空间(即它们的基向量)来提取特征和分类动作的方法,而不是投影地标数据本身。这产生动作序列的固定长度描述,而不考虑所提供帧的数量。我们给出了各种全局降维技术的比较,并分析了它们对我们提出的方案的适用性。在CMU运动捕捉数据集上进行的实验表明,在存在噪声和错误地标检测的情况下,该方法具有良好的识别率和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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