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- W4320152497 abstract "With the rapid development of computer vision and artificial intelligence, human pose estimation has become the subject of intense scholarly debate. In addition, ubiquitous video software and monitoring machines provide sufficient video data, and all kinds of key elements can be found in the visual information. However, due to different task subdivision scenarios as well as the confusing nature of the human actions, the scenario-oriented video detection techniques aim to establish standard libraries for distinct application scenarios, aggregating both original joint coordinates and composite features. In this paper, we present an innovative framework for detecting possible actions in various scenarios, table tennis training, pilot running planes and boot camp scenes, which covers vast range of social scenes. First, we call OpenPose for accurate and robust skeleton information. Then, manually constructed features of pose angles, relative displacements, moving pattern sequences and etc. are calculated. These informative features enlighten people of latent motion rules. Finally, we report how our framework is applied to realistic classification datasets. Through our work, an overall sketch for skeleton-based human pose estimation and a framework with practical application value is proposed, where people can gain a deep theoretical and practical understanding of a front field of computer vision." @default.
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- W4320152497 date "2022-01-01" @default.
- W4320152497 modified "2023-10-05" @default.
- W4320152497 title "Research and Analysis of Video-Based Human Pose Estimation" @default.
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- W4320152497 doi "https://doi.org/10.1007/978-981-19-9297-1_14" @default.
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