基于稀疏编码特征融合的交互行为识别

Interactive Behavior Recognition Based on Sparse Coding Feature Fusion

交互行为的识别是机器视觉研究领域的热点和难点,针对其识别率低的问题,提出了一种融合深度图像边缘特征、RGB(Red,Green,Blue)图像纹理特征以及光流运动轨迹特征的识别算法。首先,采用Canny算子提取深度图像的边缘特征,采用局部二值模式算子提取RGB图像的纹理特征,采用光流直方图描述图像的动态特征;然后,将提取的边缘特征和纹理特征进行加权融合;最后,利用基于稀疏表示的空间金字塔匹配模型对静态融合特征和光流运动轨迹特征进行编码融合,对交互行为进行识别。基于MSR Action Pairs、SBU Kinect interaction、CAD-60数据集的实验结果表明,本算法的识别效果较好。

Research on interactive behavior recognition has always been a research hotspot and difficulty in the field of machine vision research.For the problem of low recognition rate,this paper proposes a recognition algorithm that combines edge features of depth images,texture features of RGB(Red,Green,Blue)images,and optical flow motion trajectory features.First,Canny operator is used to extract the edge features of the depth images,local binary pattern operator is used to extract the texture features of the RGB images,and optical flow histogram is used to describe the dynamic characteristics of the images.Then,the extracted edge features and texture features are weighted and fused.Finally,static fusion feature and optical flow motion trajectory feature are coded and fused using the spatial pyramid matching model based on sparse representation to identify interactive behaviors.Experimental results based on MSR Action Pair,SBU Kinect interaction,and CAD-60 data sets show that the algorithm has a better recognition effect.