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http://dx.doi.org/10.9717/kmms.2022.25.4.575

Motion Recognition of Workers using Skeleton and LSTM  

Jeon, Wang Su (Dept. of Computer Engineering., Kyungnam University)
Rhee, Sang Yong (Dept. of Computer Engineering., Kyungnam University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.4, 2022 , pp. 575-582 More about this Journal
Abstract
In the manufacturing environment, research to minimize robot collisions with human beings have been widespread, but in order to interact with robots, it is important to precisely recognize and predict human actions. In this research, after enhancing performance by applying group normalization to the Hourglass model to detect the operator motion, the skeleton was estimated and data were created using this model. And then, three types of operator's movements were recognized using LSTM. As results of the experiment, the accuracy was enhanced by 1% using group normalization, and the recognition accuracy was 99.6%.
Keywords
Pose Estimation; Pose Classification; Hourglass; LSTM; CNN; Group Normalization;
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Times Cited By KSCI : 1  (Citation Analysis)
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