基于深度图像与骨骼数据的行为识别

Action recognition based on depth images and skeleton data

为了充分利用深度图像与骨骼数据进行人体行为识别,提出了一种基于深度图形与骨骼数据的多特征行为识别方法。该算法的多特征包括深度运动图(DMM)特征与四方形骨骼特征(Quad)。深度图像方面,将深度图像投影到一个笛卡尔坐标系的三个平面获得深度运动图特征。骨骼数据方面,提出四方形骨骼特征,它是骨骼坐标的一种标定方式,得到的结果只与骨骼姿态有关。同时提出一种多模型概率投票的分类策略,减小了噪声数据对分类结果的影响。所提方法在MSR-Action3D和DHA数据库进行实验,实验结果表明,所提算法有着较高的识别率与良好的鲁棒性。

In order to make full use of depth images and skeleton data for action detection, a multi-feature human action recognition method based on depth images and skeleton data was proposed. Multi-features included Depth Motion Map （DMM） feature and Quadruples skeletal feature （Quad）. In aspect of depth images, DMM could be captured by projecting the depth image onto the three plane of a Descartes coordinate system. In aspect of skeleton data, Quad was a kind of calibration method for skeleton features and the results were only related to the skeleton posture. Meanwhile, a strategy of multi-model probabilistic voting model was proposed to reduce the influence from noise data on the classification. The proposed method was evaluated on Microsoft Research Action3D dataset and Depth-included Human Action （DHA） database. The results indicate that the method has high accuracy and good robustness.