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<dc:title xml:lang="pl"><![CDATA[A single upper limb pose estimation method based on the improved stacked hourglass network]]></dc:title>
<dc:creator><![CDATA[Peng, Gang]]></dc:creator>
<dc:creator><![CDATA[Zheng, Yuezhi]]></dc:creator>
<dc:creator><![CDATA[Li, Jianfeng]]></dc:creator>
<dc:creator><![CDATA[Yang, Jin]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[convolutional neural networks]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[stacked hourglass network]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[skeleton key point]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[single upper limb pose estimation]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[human?machine coordination]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[For use in human-machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model is designed. A deconvolution layer is employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Integral regression is used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments show that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2021]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/86241/AMCS_2021_31_1_9.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/101333/edition/86241/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:86241]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 31, number 1 (2021)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=119]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:101333]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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