Sparse human movement representation and recognition

Nikolaos Gkalelis, A. Tefas, I. Pitas
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引用次数: 6

Abstract

In this paper a novel method for human movement representation and recognition is proposed. A movement type is regarded as a unique combination of basic movement patterns, the so-called dynemes. The fuzzy c-mean (FCM) algorithm is used to identify the dynemes in the input space and allow the expression of a posture in terms of these dynemes. In the so-called dyneme space, the sparse posture representations of a movement are combined to represent the movement as a single point in that space, and linear discriminant analysis (LDA) is further employed to increase movement type discrimination and compactness of representation. This method allows for simple Mahalanobis or cosine distance comparison of movements, taking implicitly into account time shifts and internal speed variations, and, thus, aiding the design of a real-time movement recognition algorithm.
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稀疏人体运动表征与识别
本文提出了一种新的人体运动表征与识别方法。一种运动类型被认为是基本运动模式的独特组合,即所谓的动力。使用模糊c均值(FCM)算法来识别输入空间中的动力,并允许根据这些动力来表达姿态。在所谓的动态空间中,将运动的稀疏姿态表示组合为该空间中的单个点,并进一步使用线性判别分析(LDA)来增加运动类型的区分和表示的紧凑性。该方法允许对运动进行简单的马氏或余弦距离比较,隐含地考虑到时间偏移和内部速度变化,从而帮助设计实时运动识别算法。
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