Human Pose Estimation with Multi-Camera Localization Using Multi-Objective Optimization Based on Topological Structured Learning
-
- Obo Takenori
- Tokyo Metropolitan University
-
- Hamada Kunikazu
- Tokyo Metropolitan University
-
- Eguchi Masatoshi
- Tokyo Metropolitan University
-
- Kubota Naoyuki
- Tokyo Metropolitan University
この論文をさがす
説明
<p>This paper presents a method of pose estimation with camera localization based on a genetic algorithm to derive the joint angle using an inverse kinematics model. In recent years, some open-source libraries for human skeleton tracking that can be implemented on smartphones have been released. Such software can detect joint positions of a skeleton from a 2D camera image; however, the 3D posture cannot be obtained. We therefore propose a method to estimate joint angles by using skeleton data. In the proposed approach, we use a multi-island genetic algorithm to maintain the diversity of the population and design a multi-objective function to improve the robustness. Moreover, structured learning based on topological mapping is implemented in the proposed method to enhance searching efficiency. In an experiment, the proposed method reduced the effect of outliers caused by misdetection. In addition, the structured learning was effective in decreasing the difference between skeleton data and the estimated poses.</p>
収録刊行物
-
- Journal of Advanced Computational Intelligence and Intelligent Informatics
-
Journal of Advanced Computational Intelligence and Intelligent Informatics 27 (4), 543-553, 2023-07-20
富士技術出版株式会社
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390015354565751168
-
- NII書誌ID
- AA12042502
-
- ISSN
- 18838014
- 13430130
-
- NDL書誌ID
- 032947545
-
- 本文言語コード
- en
-
- データソース種別
-
- JaLC
- NDL
- Crossref
-
- 抄録ライセンスフラグ
- 使用不可