Behavior recognition system based on an attention mechanism

A recognition system and attention technology, applied in character and pattern recognition, computer parts, instruments, etc., can solve different problems without comprehensive consideration, achieve the effect of less training time, improved classification effect, and reduced overhead

Active Publication Date: 2019-06-11
GUANGZHOU INTELLIGENT CITY DEV INST +1
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Problems solved by technology

[0009] In the existing technical scheme 2, two different aspects are not considered comprehensively: information between channels and spatial p...

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  • Behavior recognition system based on an attention mechanism
  • Behavior recognition system based on an attention mechanism
  • Behavior recognition system based on an attention mechanism

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Embodiment Construction

[0030] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0031] refer to Figure 1 to Figure 4 ,Such as Figure 1 to Figure 4 As shown, an attention mechanism-based behavior recognition system consists of input, intermediate Block, and output;

[0032] The input end is an RGB image, and the Attention Module can be arbitrarily embedded in one of the Blocks; the attention module is mainly divided into the Channel Attention module and the Spatial Attention module; Spatial Attention; After the feed operation, the convolutional neural network outputs the category of the corresponding behavior;

[0033] The entire network structure of the system is based on...

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Abstract

The invention discloses a behavior recognition system based on an attention mechanism. The behavior recognition system is composed of an input, an intermediate Block and an output. The whole network structure of the system is based on Inception V3, and two proposed Attention Modules are selected to be added into one Block; Wherein the Chosel Attention module is used for extracting dependence amongchannels, and space dependence is obtained by using Spacal Attention. The invention aims to overcome the influence of an error tag and background information. Residual learning is used to combine channel attention with spatial attention. And longer-term time information using self-attention is acquired as part of the network. In the model, the attention of the space and the channel is utilized, and only the attention of the two-dimensional channel is used in the module design.

Description

technical field [0001] The invention relates to behavior recognition, in particular to an attention mechanism-based behavior recognition system. Background technique [0002] At present, since the Convolutional Neural Network (CNN) has achieved great success in image classification, the academic community has proposed many CNN-based methods to promote the performance of action recognition. These neural networks can be classified into two types, 2D convolutional networks (usually using one RGB frame or multiple streaming frames as input) and 3D convolutional networks (using multiple frames as input and simultaneously integrating spatial and temporal information). Due to the lack of temporal information, some 2D-based CNNs use a parallel structure to process multiple RGB frames and fuse them at the last layer to obtain temporal information. [0003] By stacking a series of convolutional layers, CNN is able to capture long-range dependency information. Each learned filter in ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
Inventor 招继恩朱勇杰王国良张海谭大伦周明
Owner GUANGZHOU INTELLIGENT CITY DEV INST
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