基于深度信息的特征学习与动作识别方法

为了进行复杂交互动作识别,提出基于深度信息的特征学习方法,并使用两层分类策略解决相似动作识别问题.该方法从频域的角度分析深度图像动作序列,提取频域特征,利用VAE对特征进行空间特征压缩表示,建立HMM模拟时序变化并进行第一层动作识别.为了解决相似动作识别问题,引入三维关节点特征进行第二层动作识别.实验结果表明,两种特征在动作数据集SBU-Kinect上能够有效地表示姿态含义,策略简单有效,识别准确率较高.

Feature learning and action recognition method based on depth information

In order to recognize complex interactive actions, this paper proposed a feature learning method based on depth information, and used a two-layer classification strategy to solve similar action recognition problems. The method analyzed the depth image action sequence from the frequency domain, extracted the frequency domain features, used VAE for spatial feature compression representation, established HMM to simulate time series changes and performed the first layer action recognition. In order to solve the problem of similar actions, this paper introduced three-dimensional joint point features for the second layer action recognition. Experimental results show that these two types of features can effectively represent the meaning of gestures on the SBU-Kinect action data set, the strategy is simple and effective and can get high recognition accuracy.