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Efficient second-order minimization combined with fuzzy control-based visual tracking of a moving face

机译:有效的二阶最小化与基于模糊控制的运动面部视觉跟踪相结合

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摘要

This paper presents a visual feedback control system that is based on an efficient second-order minimization algorithm and can be used to track a moving face. This study aims to control a camera installed at the end of a three-degree-of-freedom robot arm and fix its orientation with respect to a moving face so that the face remains at a constant position in the image. In this work, the efficient second-order minimization algorithm is used to feed back the orientation of the two-dimensional human face. Then, an intelligent fuzzy algorithm is utilized by the tracking system to allow it to both track and become synchronized with the movement of the face. In addition, a Kalman filter is implemented to create an improved performance of the prediction of the position of the face. The applied control scheme is tested in tracking experiments on a moving face performed using a robot system with a camera installed on its end-effecter and the proposed scheme is shown to be an effective approach to the considered problem.
机译:本文提出了一种视觉反馈控制系统,该系统基于高效的二阶最小化算法,可用于跟踪运动面部。这项研究旨在控制安装在三自由度机械臂末端的摄像头,并相对于移动的面部固定其方向,以使面部保持在图像中的恒定位置。在这项工作中,有效的二阶最小化算法用于反馈二维人脸的方向。然后,跟踪系统利用了一种智能模糊算法,使其既可以跟踪又可以与面部运动同步。另外,实施卡尔曼滤波器以创建面部位置的预测的改进性能。所应用的控制方案是在使用机器人系统(末端安装有摄像头)的机器人系统上进行的运动面上跟踪实验中进行测试的,所提出的方案被证明是解决上述问题的有效方法。

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