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Visual learning and recognition of sequential data manifolds with applications to human movement analysis

机译：视觉学习和顺序数据流形的识别及其在人体运动分析中的应用

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Human motion analysis is increasingly attracting much attention from computer vision researchers. This paper aims to address the task of human gait and activity analysis from image sequences by learning and recognition of sequential data under a general integrated framework. Human movements generally exhibit intrinsically nonlinear spatiotemporal characteristics in the high-dimensional ambient space. An attractive framework, which we explore here, is to: (1) Extract simple and reliable features from image sequences. (2) Find a low-dimensional feature representation embedded in high-dimensional image data. (3) Then characterize/classify the motions in this low-dimensional feature space. We examine two simple alternatives for step 1: silhouette and a distance transformed silhouette; and three quite different methods for step 3: Gaussian mixture models (GMM) based classification, a matching-based approach with the mean Hausdorff distance, and continuous hidden Markov models (HMM) based modelling and recognition. The core is step 2 where we choose to use LPP (locality preserving projections), an optimal linear approximation to a nonlinear spectral embedding technique (i.e., Laplacian eigenmap). In essence our aim is to see whether this core, together with simple approaches to steps 1 and 3, can solve problems across several types of human gait and activity. To see how well the proposed framework performs, we carry out extensive experiments in three related domains: human activity recognition, abnormal gait analysis, and gait-based human identification. The experimental results show that the proposed framework performs well across all three areas.
机译：人体运动分析越来越引起计算机视觉研究人员的关注。本文旨在通过在通用集成框架下学习和识别顺序数据来解决图像序列中的步态和活动分析任务。人类运动通常在高维环境空间中表现出内在的非线性时空特性。我们在这里探索的一个有吸引力的框架是：（1）从图像序列中提取简单可靠的特征。 （2）找到嵌入高维图像数据的低维特征表示。 （3）然后对低维特征空间中的运动进行表征/分类。我们研究步骤1的两个简单替代方案：轮廓和距离变换轮廓；以及用于步骤3的三种完全不同的方法：基于高斯混合模型（GMM）的分类，具有平均Hausdorff距离的基于匹配的方法以及基于连续隐马尔可夫模型（HMM）的建模和识别。核心是第2步，我们选择使用LPP（局部保留投影），这是对非线性频谱嵌入技术（即Laplacian特征图）的最佳线性逼近。本质上，我们的目标是查看此核心以及简单的步骤1和步骤3是否可以解决多种类型的人类步态和活动问题。为了了解所提出框架的性能，我们在三个相关领域进行了广泛的实验：人类活动识别，步态异常分析和基于步态的人类识别。实验结果表明，提出的框架在所有三个方面均表现良好。

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