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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Discriminative Saliency-pose-attention Covariance for Action Recognition
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Discriminative Saliency-pose-attention Covariance for Action Recognition

机译：动作识别的显着性-姿势-注意协方差

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摘要

Most covariance-based representations of actions are focused on the statistical features of poses by empirical averaging weighting. Note that these poses have a variety of saliency levels for different actions. Neglecting pose saliency could degrade the discriminative power of the covariance features, and further reduce the performance of action recognition. In this paper, we propose a novel saliency weighting covariance feature representation, Saliency-Pose-Attention Covariance(SPA-Cov), which reduces the negative effects from the ambiguous pose samples. Specifically, we utilize a discriminative approach to derive probability distribution of action categories for each pose, which is modeled by the uncertainty of information entropy to obtain the salient weighting. Experimental results show that our proposed method efficiently improves the discriminative power of the generated covariance. In some databases, the proposed SPA-Cov outperforms the state-of-the-art variant methods which are based on kernel matrix, Bayesian posterior features, temporal hierarchical features, etc.
机译：基于协方差的行动表示通过经验平均权重统治着姿势的统计特征。请注意，这些姿势具有不同动作的各种显着水平。忽略构成显着性可能降低协方差特征的辨别力，并进一步降低了动作识别的性能。在本文中，我们提出了一种新的显着权重协方差特征表示，显着性姿势协方差（SPA-COV），其降低了模糊姿势样本的负面影响。具体地，我们利用鉴别方法来获得每个姿势的动作类别的概率分布，这是由信息熵的不确定性来建模的，以获得突出的重量。实验结果表明，我们所提出的方法有效地提高了所产生的协方差的辨别力。在某些数据库中，所提出的SPA-COV优于基于内核矩阵，贝叶斯后部特征，时间分层特征等的最先进的变体方法。

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