Research on key issues of gesture recognition for artificial intelligence
Gesture recognition has become a hot spot in the direction of artificial intelligence and has great research significance. At present, some classical algorithms, such as the neural network method and the hidden Markov method, have the disadvantages of large computational complexity and long training...
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Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2020-04, Vol.24 (8), p.5795-5803 |
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description | Gesture recognition has become a hot spot in the direction of artificial intelligence and has great research significance. At present, some classical algorithms, such as the neural network method and the hidden Markov method, have the disadvantages of large computational complexity and long training time. This paper proposes the support vector machine (SVM) algorithm to realize gesture recognition. In order to make the recognition more accurate, SVM is combined with the principal component analysis (PCA) algorithm, performs the dimensionality reduction on the gesture image to form the PCA + SVM algorithm for gesture recognition. At the same time, a new dynamic gesture recognition processing method is proposed, and its effectiveness is proved by various methods. Using open-source computer vision library (OPENCV), the algorithm is simulated on visual studio 2015 environment. The results show that the algorithm has an excellent recognition effect. |
doi_str_mv | 10.1007/s00500-019-04342-3 |
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At present, some classical algorithms, such as the neural network method and the hidden Markov method, have the disadvantages of large computational complexity and long training time. This paper proposes the support vector machine (SVM) algorithm to realize gesture recognition. In order to make the recognition more accurate, SVM is combined with the principal component analysis (PCA) algorithm, performs the dimensionality reduction on the gesture image to form the PCA + SVM algorithm for gesture recognition. At the same time, a new dynamic gesture recognition processing method is proposed, and its effectiveness is proved by various methods. Using open-source computer vision library (OPENCV), the algorithm is simulated on visual studio 2015 environment. 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At present, some classical algorithms, such as the neural network method and the hidden Markov method, have the disadvantages of large computational complexity and long training time. This paper proposes the support vector machine (SVM) algorithm to realize gesture recognition. In order to make the recognition more accurate, SVM is combined with the principal component analysis (PCA) algorithm, performs the dimensionality reduction on the gesture image to form the PCA + SVM algorithm for gesture recognition. At the same time, a new dynamic gesture recognition processing method is proposed, and its effectiveness is proved by various methods. Using open-source computer vision library (OPENCV), the algorithm is simulated on visual studio 2015 environment. The results show that the algorithm has an excellent recognition effect.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Computer vision</subject><subject>Control</subject><subject>Engineering</subject><subject>Focus</subject><subject>Gesture recognition</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Neural networks</subject><subject>Principal components analysis</subject><subject>Robotics</subject><subject>Support vector machines</subject><subject>Visual programming languages</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMtKAzEUhoMoWKsv4CrgOnpymzRLKV4KBUF0HdL0zJg6ztRkuujbm3YEd67OWXz_uXyEXHO45QDmLgNoAAbcMlBSCSZPyIQrKZlRxp4ee8FMpeQ5uch5AyC40XJCFq-Y0afwQfuOfuKexpx3mGlf0wbzsEtIE4a-6eIQC1H3ifo0xDqG6FsauwHbNjbYBbwkZ7VvM1791il5f3x4mz-z5cvTYn6_ZEFyOzAfuJE6-FBxoVHXWqiwtjNfWTOTBmDFrTV4-GYWglRK-JpbXq20kWsb7EpOyc04d5v673Lq4Db9LnVlpROWGwuVNlAoMVIh9TknrN02xS-f9o6DOyhzozJXlLmjMidLSI6hXOCuwfQ3-p_UD0PLbeE</recordid><startdate>20200401</startdate><enddate>20200401</enddate><creator>Mo, Taiping</creator><creator>Sun, Peng</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20200401</creationdate><title>Research on key issues of gesture recognition for artificial intelligence</title><author>Mo, Taiping ; Sun, Peng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-ac1735cac6125e5f524cd98a69783700b1997e05008cc3442af1916b573d9c9b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computational Intelligence</topic><topic>Computer vision</topic><topic>Control</topic><topic>Engineering</topic><topic>Focus</topic><topic>Gesture recognition</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Neural networks</topic><topic>Principal components analysis</topic><topic>Robotics</topic><topic>Support vector machines</topic><topic>Visual programming languages</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mo, Taiping</creatorcontrib><creatorcontrib>Sun, Peng</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mo, Taiping</au><au>Sun, Peng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Research on key issues of gesture recognition for artificial intelligence</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2020-04-01</date><risdate>2020</risdate><volume>24</volume><issue>8</issue><spage>5795</spage><epage>5803</epage><pages>5795-5803</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>Gesture recognition has become a hot spot in the direction of artificial intelligence and has great research significance. 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subjects | Algorithms Artificial Intelligence Computational Intelligence Computer vision Control Engineering Focus Gesture recognition Mathematical Logic and Foundations Mechatronics Neural networks Principal components analysis Robotics Support vector machines Visual programming languages |
title | Research on key issues of gesture recognition for artificial intelligence |
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