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Automatic zooming mechanism for capturing high object image using high definition fixed camera

机译：使用高清固定摄像机捕获高物体图像的自动变焦机制

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摘要

High definition (HD) camera is widely used in surveillance systems. An HD camera with optical zoom is useful for monitoring a large area. However, it is inconvenient for a user to manually control the optical zoom for a long time. To exploit the functionality and extend the application domains of a HD camera, the zooming should be controlled automatically. An automatic zooming mechanism is proposed in this paper. When an object moves through the field of view (FOV) of the camera, the zoom is controlled for capturing the object as clear as possible. In order to achieve the above goal, a Gaussian Mixture Model (GMM), temporal image differencing, a CamShift tracking method, and a Kalman filter are utilized for object detection and tracking. Then, an adaptive neuro-fuzzy inference system (ANFIS) is used to learn and determine a suitable value for adjusting the zoom. According to the experimental study of the prototype, the results show that the proposed mechanism is useful to capture the clear images of moving objects in a practical environment.
机译：高清（HD）摄像机广泛用于监视系统。具有光学变焦的高清摄像机对于监视大面积区域非常有用。但是，用户长时间手动控制光学变焦是不方便的。要利用此功能并扩展高清摄像机的应用范围，应自动控制缩放。本文提出了一种自动缩放机制。当物体在相机的视场（FOV）中移动时，将控制变焦以捕获尽可能清晰的物体。为了实现上述目标，将高斯混合模型（GMM），时间图像差分，CamShift跟踪方法和卡尔曼滤波器用于对象检测和跟踪。然后，使用自适应神经模糊推理系统（ANFIS）来学习和确定用于调整缩放的合适值。根据对原型的实验研究，结果表明，该机制对于在实际环境中捕获运动物体的清晰图像非常有用。

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