Human body behavior recognition method based on sparse tensor local Fisher discriminant analysis algorithm

A technology of discriminant analysis and recognition method, which is applied in character and pattern recognition, calculation, computer parts and other directions, and can solve the problem of destroying the spatial correlation of images

Active Publication Date: 2019-10-15
HOHAI UNIV
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Due to the eigenvalue decomposition of a large matrix, this operation will not only ca...

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  • Human body behavior recognition method based on sparse tensor local Fisher discriminant analysis algorithm
  • Human body behavior recognition method based on sparse tensor local Fisher discriminant analysis algorithm
  • Human body behavior recognition method based on sparse tensor local Fisher discriminant analysis algorithm

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Embodiment Construction

[0094] The technical solution of the present invention will be further described below in conjunction with the drawings and embodiments.

[0095] The human body behavior recognition method based on the sparse tensor local Fisher discriminant analysis algorithm of the present invention includes the following steps:

[0096] S1: Obtain the silhouette image sequence according to the Weizmann human body behavior database, and construct a tensor sample. The 10 different actions in the human body behavior database correspond to 10 types of tensor samples, and the training sample set and the sample set to be tested are constructed according to the tensor sample ;

[0097] S2: Use the sparse tensor local Fisher discriminant analysis algorithm (STLFDA) to train a sparse projection matrix group according to the training sample set; the sparse projection matrix group can project the original tensor sample from the high-dimensional space to the low-dimensional quantum space, To ensure that the ...

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Abstract

The invention discloses a human body behavior recognition method based on sparse tensors. The human body behavior recognition method comprises the following steps: obtaining a human body behavior silhouette sequence construction sample, and representing each sample with a three-order tensor; using sparse tensor local Fisher discriminant analysis to train a sparse projection matrix group; projecting the training sample and the to-be-tested sample to a tensor subspace by using the obtained sparse projection matrix group; and calculating the similarity between the to-be-tested sample and the training sample, and constructing a nearest neighbor classifier based on the tensor distance to identify the to-be-tested sample in the low-dimensional space. According to the tensor local Fisher discriminant analysis algorithm of the invention, the problems of eigenvalue solving and eigenvector solving are converted into a series of linear regression problems, so that the objective of tensor local Fisher discriminant analysis can be satisfied, and the sparsity of an obtained projection matrix can be ensured.

Description

Technical field [0001] The present invention relates to the technical field of feature extraction of human behavior silhouette sequences, in particular to a human behavior recognition method based on a sparse tensor local Fisher discriminant analysis algorithm. Background technique [0002] The basic idea of ​​the extraction method based on algebraic features is to project the original samples into a subspace to form algebraic features. Representative methods include principal component analysis (PCA), linear discriminant analysis (LDA) and methods based on manifold learning. Principal component analysis (PCA) is an unsupervised feature extraction method that aims to maximize the variance of the projection subspace by maximizing the trace of the covariance matrix. The physical meaning is to make the distance between all samples as large as possible in the subspace after projection. Linear Discriminant Analysis (LDA) can ensure that the inter-class dispersion is maximized in the ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06F18/24147
Inventor 卢雨彤韩立新
Owner HOHAI UNIV
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