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基于流形学习的人体动作识别

DOI: 10.11834/jig.20140612

Keywords: Kinectsensor,人体动作识别,流形学习,Hausdorff距离,深度数据

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Abstract:

目的提出了一个基于流形学习的动作识别框架,用来识别深度图像序列中的人体行为。方法从Kinect设备获得的深度信息中评估出人体的关节点信息,并用相对关节点位置差作为人体特征表达。在训练阶段,利用LE(Lalpacianeigenmaps)流形学习对高维空间下的训练集进行降维,得到低维隐空间下的运动模型。在识别阶段,用最近邻差值方法将测试序列映射到低维流形空间中去,然后进行匹配计算。在匹配过程中,通过使用改进的Hausdorff距离对低维空间下测试序列和训练运动集的吻合度和相似度进行度量。结果用Kinect设备捕获的数据进行了实验,取得了良好的效果;同时也在MSRAction3D数据库上进行了测试,结果表明在训练样本较多情况下,本文方法识别效果优于以往方法。结论实验结果表明本文方法适用于基于深度图像序列的人体动作识别。

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