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A Neural Network Gait Recognition Method Based on Attention Mechanism

A neural network and gait recognition technology, applied in biological neural network models, neural architecture, character and pattern recognition, etc., can solve the problem of insufficient utilization of attention mechanism information

Active Publication Date: 2021-08-31
BEIJING JIAOTONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the current gait recognition method based on the attention mechanism, the attention mechanism constructed has problems such as insufficient information utilization, and there is a certain room for improvement.

Method used

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  • A Neural Network Gait Recognition Method Based on Attention Mechanism
  • A Neural Network Gait Recognition Method Based on Attention Mechanism
  • A Neural Network Gait Recognition Method Based on Attention Mechanism

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Embodiment Construction

[0029] The gait recognition method based on the attention mechanism of the present invention uses the CASIA-B gait data set issued by the Chinese Academy of Sciences to conduct experiments. Specifically include the following steps:

[0030] (1) Training a gait feature extraction model based on the attention mechanism.

[0031] 1.1) Split the training set and test set from the benchmark dataset CASIA-B.

[0032] 1.2) The input size of the 3D convolutional neural network is set as B*C*T*H*W, where B represents the batch dimension, C represents the number of channels of the input gait image, and T represents the frame length of the input gait video sequence , H and W are the length and width of each frame of gait video sequence. In this method, the samples are normalized to a size of 64*44.

[0033] 1.3) Through the iterative optimization strategy, the samples and sample labels are used to pre-train the gait feature extraction model, so that the trained gait feature extraction...

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Abstract

The present invention provides a gait recognition method based on the attention mechanism, comprising the following steps: segmenting a training set and a test set from a benchmark data set; extracting a model pre-training network through a gait not embedded in an attention mechanism, so as to Make the model have good adaptability to human gait; embed the temporal and spatial attention mechanism modules in the network, and load the pre-trained network model parameters; re-use the data set to train the gait recognition feature extraction model based on the attention mechanism , so that better gait recognition results can be obtained.

Description

technical field [0001] The invention belongs to the field of gait recognition in pattern recognition, and relates to a gait recognition method based on time domain attention and space domain attention. Background technique [0002] Gait recognition is a biometric technology that recognizes the posture of the human body when walking. Different from traditional biometric technology, gait recognition technology has the advantages of not requiring the cooperation of subjects and being able to identify from a distance. At present, gait recognition is widely used in many fields such as access control monitoring and identity authentication, and human gait is not easy to disguise and has uniqueness, which is helpful for accurate identification. It has broad application prospects. [0003] A typical gait recognition system mainly includes three parts, namely gait image preprocessing, human gait feature extraction and recognition classification based on gait features. First of all,...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/25G06N3/045G06F18/214
Inventor 张顺利林贝贝
Owner BEIJING JIAOTONG UNIV