Laser & Optoelectronics Progress, Volume. 57, Issue 18, 181006(2020)
Interactive Behavior Recognition Based on Sparse Coding Feature Fusion
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.
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Jianjun Li, Yue Sun, Baohua Zhang. Interactive Behavior Recognition Based on Sparse Coding Feature Fusion[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181006
Category: Image Processing
Received: Dec. 11, 2019
Accepted: Feb. 11, 2020
Published Online: Sep. 2, 2020
The Author Email: Sun Yue (sunyueya0526@163.com)