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Multi-feature fusion sign language recognition method based on adaptive hidden markov

A Hidden Markov, adaptive technology, applied in the field of computer vision, which can solve problems such as high overhead and reduced effect

Active Publication Date: 2019-03-01
HEFEI UNIV OF TECH
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  • Abstract
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AI Technical Summary

Problems solved by technology

Back-end score fusion usually takes too much time, and in different models, features with poor effects may dominate feature fusion, reducing the effect of fusion

Method used

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  • Multi-feature fusion sign language recognition method based on adaptive hidden markov
  • Multi-feature fusion sign language recognition method based on adaptive hidden markov
  • Multi-feature fusion sign language recognition method based on adaptive hidden markov

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Embodiment Construction

[0059] In this example, if figure 1 As shown, a multi-feature fusion sign language recognition method based on adaptive hidden Markov, this method uses Gaussian mixture-hidden Markov model GMM-HMM, firstly extracts multiple features from the sign language video database and performs front-end fusion, namely Construct a feature pool set; then build an adaptive hidden Markov model for each sign language video under different features in the feature pool set, and propose a feature selection strategy to obtain a suitable back-end score fusion feature; choose a good back-end After the score fusion feature, the score vector under each back-end score fusion feature is calculated, and different weights are assigned to it, and then the back-end score fusion is performed to obtain the optimal fusion effect. Specifically, if figure 1 shown, including the following steps:

[0060] Step 1, obtain the sign language video database, and divide the sign language video in the sign language vi...

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Abstract

The invention discloses a multi-feature fusion sign language recognition method based on adaptive hidden markov, which comprises the following steps: 1. Firstly, extracting multiple features from a sign language video database and performing front-end fusion, namely constructing a feature pool set; then, an adaptive hidden Markov model of sign language video under different features in the featurepool set is constructed, and a feature selection strategy to obtain the appropriate back-end score fusion features. After selecting the back-end scoring fusion features, the scoring vectors under each back-end scoring fusion feature are calculated, and different weights are assigned to them, and then the back-end scoring fusion is carried out, so as to obtain the optimal fusion effect. The invention can realize the accurate recognition of sign language categories in sign language video and improve the robustness of the recognition.

Description

technical field [0001] The invention belongs to the technical field of computer vision, relates to technologies such as pattern recognition and artificial intelligence, and specifically relates to a multi-feature fusion sign language recognition method based on adaptive hidden Markov. technical background [0002] Deaf people are a large group of disabled people, because they cannot speak, deaf people usually use sign language as a means of communication. When normal people who have not learned sign language need to communicate with deaf people, communication barriers arise, and most normal people in society have not received sign language education. Therefore, the sign language interpretation system is of great significance to the deaf-mute as an auxiliary way to facilitate the integration of the deaf-mute into the society. But at present, sign language translation is still a difficult problem in the field of computer vision. The reason is that there are many factors such ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/28G06V20/41G06F18/23G06F18/253
Inventor 郭丹宋培培赵烨汪萌
Owner HEFEI UNIV OF TECH
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