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Silhouette analysis for human action recognition based on maximum spatio-temporal dissimilarity embedding

机译:基于最大时空差异嵌入的人体动作识别轮廓分析

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摘要

In this paper, we present a human action recognition method for human silhouette sequences. Inspired by the locality preserving projection and its variants, a novel manifold embedding method, maximum spatio-temporal dissimilarity embedding, is proposed to embed each action frame into a manifold, where frames from different action classes can be well separated. Unlike existing methods that incorporate both inter-class and intra-class information in the embedding process, our proposed method focuses on maximizing distances between frames that are similar in appearance but are from different classes and takes the temporal information into consideration. A variant of Hausdorff distance is introduced for frame and sequence classifications. Extensive experimental results and comparison with state-of-the-art methods demonstrate the effectiveness and robustness of the proposed method for human action silhouette analysis.
机译:在本文中,我们提出了一种用于人体轮廓序列的人体动作识别方法。受到局部性保留投影及其变体的启发,提出了一种新颖的流形嵌入方法,即最大时空不相似性嵌入,将每个动作帧嵌入到一个流形中,可以将不同动作类别的帧很好地分开。与现有的在嵌入过程中同时包含类内和类内信息的方法不同,我们提出的方法着重于使外观相似但来自不同类的帧之间的距离最大化,并考虑了时间信息。介绍了Hausdorff距离的一种变体,用于帧和序列分类。大量的实验结果以及与最先进方法的比较证明了所提出的人体动作轮廓分析方法的有效性和鲁棒性。

著录项

  • 来源
    《Machine Vision and Applications》 |2014年第4期|1007-1018|共12页
  • 作者单位

    School of Electronic Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., Chengdu 611731, China;

    School of Electronic Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., Chengdu 611731, China;

    School of Electronic Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., Chengdu 611731, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Silhouette analysis; Human action recognition; Gait recognition; Manifold learning; Hausdorff distance;

    机译:轮廓分析;人体动作识别;步态识别;流形学习;Hausdorff距离;

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