为解决传统相机不能在全景范围内快速连续跟踪运动物体的弊端，提出一种仿生复眼式全景探测思想，以及配套的跟踪策略。在硬件方面，采用多个子眼相机进行全景实时探测，中心采用一个大孔径主眼相机，并且将其安装于云台之上，通过云台控制器进行精确定位。在软件方面，采用自动加窗采集的思想，对于运动区域的图像进行多层窗口采集，非运动区域则减少窗口层数，然后用高斯运动模型算法对采集的图像进行处理。此外，在重叠区采用的是仿生复眼的侧抑制的检测方法，从而得到比较清晰的运动目标轮廓。按照本文软硬件设计思想，搭建实际实验装置，并进行重复性实验。由于实时探测特点，本文方法结合加窗采集和侧抑制算法相结合的思想，相比传统相机的跟踪效果，在视场和灵敏度上得到了很大的提高。

To solve the disadvantages that a traditional camera cannot track moving objects in panoramic range, this paper presents an idea about bionic compound eye panoramic detection and a related tracking strategy. On the hardware side, we use multiple sub-eye cameras to detect the whole field of view in real-time, while the center of sub-eye cameras is a large-aperture dominant eye cameras which is installed on the pan tilt and located precisely by a pan tilt controller that receives different codes. On the software side, we present an idea about automatic windowing collection. Multiple layers of windows are collected from the image of the movement area, while non-sport areas have fewer layers windows. Then, we use the method of the Gaussian model to detect the moving object. The detection method is using lateral inhibition in the overlap area to obtain the clear moving object silhouette. In accordance with the above ideas on the hardware and software sides, we build the actual experimental device, and do experiments repeatedly. Compared to the traditional camera's tracking performance, this camera has some characteristics are that the real-time detection and the combination of the windowing acquisition and lateral inhibition, so we obtain a greatly improvement in the field of view and sensitivity.