JACIII Vol.27 No.4 pp. 543-553
doi: 10.20965/jaciii.2023.p0543

Research Paper:

Human Pose Estimation with Multi-Camera Localization Using Multi-Objective Optimization Based on Topological Structured Learning

Takenori Obo ORCID Icon, Kunikazu Hamada, Masatoshi Eguchi, and Naoyuki Kubota ORCID Icon

Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

Corresponding author

December 26, 2022
February 21, 2023
July 20, 2023
human pose estimation, camera localization, structured learning, multi-objective optimization, topological mapping

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.

Pose estimation with camera localization

Pose estimation with camera localization

Cite this article as:
T. Obo, K. Hamada, M. Eguchi, and N. Kubota, “Human Pose Estimation with Multi-Camera Localization Using Multi-Objective Optimization Based on Topological Structured Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 543-553, 2023.
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Last updated on Sep. 29, 2023