• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.53-59
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• 2024

This study presents the design and implementation of a camera-based 6DoF (6 Degrees of Freedom) tracking and pose estimation system. In particular, we propose a method for accurately estimating the positions and orientations of all fingers of a user utilizing a 6DoF robotic arm. The system is developed using the Python programming language, leveraging the Mediapipe and OpenCV libraries. Mediapipe is employed to extract keypoints of the fingers in real-time, allowing for precise recognition of the joint positions of each finger. OpenCV processes the image data collected from the camera to analyze the finger positions, thereby enabling pose estimation. This approach is designed to maintain high accuracy despite varying lighting conditions and changes in hand position. The proposed system's performance has been validated through experiments, evaluating the accuracy of hand gesture recognition and the control capabilities of the robotic arm. The experimental results demonstrate that the system can estimate finger positions in real-time, facilitating precise movements of the 6DoF robotic arm. This research is expected to make significant contributions to the fields of robotic control and human-robot interaction, opening up various possibilities for future applications. The findings of this study will aid in advancing robotic technology and promoting natural interactions between humans and robots.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.942-956
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• 2014

This paper proposes a real-time hand pose tracking method using a monocular RGB camera. Hand tracking has high ambiguity since a hand has a number of degrees of freedom. Thus, to reduce the ambiguity the proposed method adopts the step-by-step estimation scheme: a palm pose estimation, a finger yaw motion estimation, and a finger pitch motion estimation, which are performed in consecutive order. Assuming a hand to be a plane, the proposed method utilizes a planar hand model, which facilitates a hand model regeneration. The hand model regeneration modifies the hand model to fit a current user's hand, and improves robustness and accuracy of the tracking results. The proposed method can work in real-time and does not require GPU-based processing. Thus, it can be applied to various platforms including mobile devices such as Google Glass. The effectiveness and performance of the proposed method will be verified through various experiments.