The emerging cost-effective depth sensors have made easier the action recognition task significantly. In this paper, we propose an effective method to analysis human actions from depth video sequences based on multi-scaling and multi-directional transformation which provide additional body shape and motion information for action recognition. In our method, corresponding to the front, side and top projection views, we generate three Depth Motion Maps (DMMs) over the entire video sequences. More specially, the multi-scaling and multi-directional transformations are implemented on the generated DMMs of a depth video sequence. Finally, the concatenation of these features is used as a feature descriptor for the depth video sequence. With this new feature descriptor, the l2-regularized collaborative representation classifier (l2- CRC) is utilized to recognize human actions. The recognition results of Microsoft Research (MSR) Action3D dataset show that our method significantly outperforms than the other existing methods, although our representation is much more compact.
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机译:新兴的具有成本效益的深度传感器大大简化了动作识别任务。在本文中,我们提出了一种有效的方法,该方法可以基于多尺度和多方向变换从深度视频序列中分析人体动作,从而为动作识别提供额外的身体形状和动作信息。在我们的方法中,对应于正面,侧面和顶部投影视图,我们在整个视频序列上生成了三个深度运动图(DMM)。更特别地,在深度视频序列的生成的DMM上实现多尺度和多方向变换。最后,这些特征的串联用作深度视频序列的特征描述符。有了这个新的特征描述符,即可使用l2标准化的协作表示分类器(l2- CRC)来识别人类行为。 Microsoft Research(MSR)Action3D数据集的识别结果表明,尽管我们的表示形式更紧凑,但是我们的方法明显优于其他现有方法。
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