基于扩展LC-KSVD的判别键姿态提取用于动作识别

Lijuan Zhou, W. Li, Yuyao Zhang, P. Ogunbona, D. Nguyen, Hanling Zhang
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引用次数: 77

摘要

提出了一种基于骨架的动作识别中可判别关键姿态的提取方法。姿态由规范化的关节位置、骨骼关节的速度和加速度表示。提出了一种扩展的标签一致K-SVD (ELC-KSVD)算法,用于通用字典和特定动作字典的学习。判别键姿势由特定动作字典的原子表示。在特定字典的基础上,通过最大池化和时间金字塔的方法获得了表示动作实例的稀疏代码。训练支持向量机分类器进行动作识别。在MSRC-12手势和MSR-Action 3D数据集上对该方法进行了评估。实验结果表明,该方法能够有效地提取识别关键姿态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discriminative Key Pose Extraction Using Extended LC-KSVD for Action Recognition
This paper presents a method for extracting discriminative key poses for skeleton-based action recognition. Poses are represented by normalized joint locations, velocities and accelerations of skeleton joints. An extended label consistent K-SVD (ELC-KSVD) algorithm is proposed for learning the common and action-specific dictionaries. Discriminative key poses are represented by the atoms of the action-specific dictionaries. With the specific dictionaries, sparse codes are obtained for representing action instances through max pooling and temporal pyramid. A SVM classifier is trained for action recognition. The proposed method was evaluated on the MSRC-12 gesture and MSR-Action 3D datasets. Experimental results have shown that the proposed method is effective in extracting discriminative key poses.
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