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
CN106485212BInactive Publication Date: 2020-01-24TIANJIN UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
TIANJIN UNIV
Publication Date
2020-01-24
Estimated Expiration
Not applicable Β· inactive patent

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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 Γ— I 2 Represents the size of the video frame; perform Tucker decomposition on the third-order tensor Ξ‘ 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.
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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...

Claims

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