A Tensor Decomposition Method for Gesture Recognition in Unequal Length Videos

A tensor decomposition and gesture recognition technology, applied in character and pattern recognition, instruments, calculations, etc., to reduce difficulty, improve accuracy, and improve accuracy

Inactive Publication Date: 2020-01-24
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a tensor decomposition method for non-equal-length video gesture recognition that can solve the problem of gesture video sequence recognition and improve the accuracy of video sequence classification

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  • A Tensor Decomposition Method for Gesture Recognition in Unequal Length Videos
  • A Tensor Decomposition Method for Gesture Recognition in Unequal Length Videos
  • A Tensor Decomposition Method for Gesture Recognition in Unequal Length Videos

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Embodiment Construction

[0045] A tensor decomposition method for non-equal-length video gesture recognition of the present invention will be described in detail below in conjunction with an embodiment.

[0046] A kind of tensor decomposition method aiming at non-equal-length video gesture recognition of the present invention comprises the following steps:

[0047] 1) Represent an original video as a third-order tensor where I 1 , I 2 and T represent the width, height and video frame number of a video respectively, and I 1 × I 2 Indicates the size of the video frame;

[0048] 2) Perform Tucker decomposition on the third-order tensor Α to obtain a nuclear tensor with reduced dimensions The nuclear tensor of the third-order tensor A Written as follows:

[0049]

[0050] in, u 3 ∈ RK×T , let the tensor F=Α× 1 u 1 x 2 u 1 , then the kernel tensor Abbreviated as:

[0051]

[0052] 3) Solve the nuclear tensor, and update the result through dynamic learning until the algorithm conve...

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Abstract

A tensor decomposition method for non-equal-length video gesture recognition, including: representing an original video as a third-order tensor where I 1 , I 2 and T represent the width, height and video frame number of a video respectively, and I 1 × I 2 Represents the size of the video frame; perform Tucker decomposition on the third-order tensor Α to obtain a nuclear tensor with reduced dimensions to solve the nuclear tensor, and update the result through dynamic learning until the algorithm convergence result reaches the optimum; repeat the above process to represent all the videos as the same The size of the kernel tensor, that is, to scale the video sequence to the same scale; realize tensor decomposition under non-equal length conditions. The invention reduces the difficulty of gesture recognition, improves the accuracy of gesture recognition, provides good conditions for subsequent video sequence classification, and can greatly improve the accuracy of video sequence classification.

Description

technical field [0001] The present invention relates to a tensor decomposition method. In particular, it concerns a tensor decomposition method for gesture recognition in unequal-length videos. Background technique [0002] With the dramatic increase in the number of available videos, content-based video analysis has been widely applied to video retrieval, action recognition, video summarization, etc. Hands and fingers have high dexterity compared to other parts of the human body, so it is one of the most efficient and versatile ways to interact with the outside world. As an important part of the semantic analysis of human actions, gesture recognition has been widely used. For example, in a hospital visit, a gesture recognition system can allow doctors to manipulate digital images through gestures. [0003] The goal of gesture recognition is to correctly classify gesture video sequences. Although there have been some works on gesture recognition, there are still many cha...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06V40/28
Inventor 苏育挺王慧晶井佩光张静
Owner TIANJIN UNIV
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