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Skeleton action recognition method based on multi-stream spatial attention graph convolution SRU network

An action recognition and attention technology, applied in the field of pattern recognition, can solve the problems of lack of consideration of bone structure connection, single use of bone data, etc., and achieve the effect of improving the efficiency of action recognition

Pending Publication Date: 2021-04-30
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, this method lacks consideration of the structural connection between bones, and the use of bone data is relatively simple.

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  • Skeleton action recognition method based on multi-stream spatial attention graph convolution SRU network
  • Skeleton action recognition method based on multi-stream spatial attention graph convolution SRU network
  • Skeleton action recognition method based on multi-stream spatial attention graph convolution SRU network

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Embodiment Construction

[0021] The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and provides detailed implementation plans and specific operating procedures.

[0022] Such as figure 1 , the method of the present invention mainly includes three steps: (1) the multi-stream data fusion method processes the original data; (2) replaces the fully connected calculation in the SRU gate structure with the graph convolution calculation, and constructs a graph convolution simple loop network (GC -SRU); (3) Introduce the spatial attention mechanism in the GC-SRU network, and finally get the result of action classification.

[0023] Each step will be described in detail below one by one.

[0024] step one:

[0025] The present invention uses 4 modes of data streams, which are node streams with original joint point coordinates as input, bone st...

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Abstract

The invention provides a skeleton action recognition method based on a multi-stream spatial attention graph convolution SRU network. The method comprises the following steps: firstly, embedding a graph convolution operator into a simple cycle unit to construct a graph convolution model to capture time-space domain information of skeleton data; meanwhile, in order to enhance the discrimination between the joint points, a spatial attention network and a multi-stream data fusion mode are designed, and a graph convolution simple cycle network model is further expanded into a multi-stream spatial attention graph convolution SRU. According to the method, the high classification precision is maintained, the complexity of the method is remarkably reduced, the reasoning speed of the model is increased, the balance between the calculation efficiency and the classification precision is achieved, and the method has a very wide application prospect.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and is a method for modeling skeleton data and classifying actions by using graph convolution and a simple recurrent unit (SRU) in combination with a spatial attention mechanism and a multi-stream data fusion method. Background technique [0002] The recognition of human actions is a basic yet challenging task in computer vision, which has facilitated the generation of many applications, such as intelligent video surveillance, human-computer interaction, video summarization and understanding, abnormal behavior detection, etc. Compared with the traditional method of using RGB image stream or video stream for action recognition, skeleton-based action recognition is not limited by background clutter, illumination changes, etc., and the representation of target actions is more robust. Most of the early skeleton-based action recognition methods simply used the joint point coordinates to construct a ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/20G06N3/045G06F18/24G06F18/251
Inventor 赵俊男佘青山陈云马玉良梅从立
Owner HANGZHOU DIANZI UNIV
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