Research on Three-Dimensional "High Presence Relay" System

A)在本研究中，我们提出了一种基于K-均值聚类的目标跟踪算法(称为K-均值跟踪器)，该算法在跟踪带有可以看到背景的洞(如蚊子圈)的目标时具有较强的鲁棒性。本研究的贡献包括：1)使用一个5D特征向量来统一表示每个像素的Y、U、V颜色特征和x，y几何特征。这使得在跟踪过程中能够同时适应颜色和几何特征；2)使用可变椭圆模型来描述搜索区域并对周围背景进行建模。这保证了目标在各种几何变换下的稳定跟踪；3)实现了对目标和背景样本的自动自跟踪故障检测和恢复。此外，我们还提出了一种基于可靠性的K-Means聚类算法(称为RK-Means跟踪器)来去除噪声背景像素(既不像目标也不像背景SAM…更多示例)来自目标对象。根据未知像素与其最近的两个聚类中心(目标和背景)之间的三角关系，将正常像素(目标或背景)赋予较高的可信度并进行正确分类，而将噪声像素给予较低的可信度并忽略。提出了一种高性能的目标跟踪系统，通过控制固定视点云台摄像机(FV-PTZ摄像机)获得高质量的视频速率下运动目标的图像。为了实现我们的目标，我们使用K均值跟踪算法来跟踪活动摄像机捕获的图像序列中的目标。利用K均值跟踪器的跟踪结果，采用PID控制方案，对各个平移变焦单元的角位置和速度进行控制。此外，通过使用两个摄像头，双目立体视觉算法可以获得物体的三维位置和速度。这些结果用于调整焦距和缩放。我们的系统允许两个摄像头在3D空间中凝视一个点。然而，当时间响应因相互控制回路中的过度干扰或严格限制相机动作而恶化时，该系统可能变得不稳定。为了解决这些问题，我们在K-Means跟踪器中引入了可靠性的概念，并提出了一种利用相对可靠性来控制活动摄像机的方法。较少

A) In this research, we propose a K-means clustering based target tracking algorithm (called as K-means tracker), which can work robustly when tracking an object with hole through which the background can be seen (e.g. mosquito coil). The contributions of this research include: 1) Using a 5D feature vector to represent both the color "Y,U,V" and geometric "x,y" features of each pixel uniformly. This enables the simultaneous adaptation to both the color and geometric features during tracking; 2) Using a variable ellipse model to describe the search area and model the surrounding background. This guarantees the stable object tracking under various geometric transformations; 3) Achieving the automatic self tracking failure detection and recovery with both the "target" and "background" samples. Moreover, we propose a reliability-based K-means clustering algorithm (called as RK-means tracker) to remove the noise background pixel (which is neither similar to the target nor the background sam … More ples) from the target object. According to the triangular relationship among an unknown pixel and its two nearest cluster centers (target and background), the normal pixel (target or background one) will be assigned with high reliability value and correctly classified, while noise pixels will be given low reliability value and ignored. A radial sampling method is also brought out for improving both the processing speed and the robustness of this algorithm.B) In this research, we propose a high-performance object tracking system for obtaining high-quality images of a high-speed moving object at video rate by controlling a Fixed Viewpoint Pan Tilt Camera (FV-PTZ Camera) . To achieve our goal, we use the K-means tracker algorithm for tracking objects in an image sequence captured by the active cameras. We use the results of the K-means tracker to control the angular position and speed of each pan-tilt-zoom unit by employing the PID control scheme. Moreover, by using two cameras, the binocular stereo vision algorithm can be used to obtain the 3D position and velocity of the object. These results are used in order to adjust the focus and zoom. Our system allows the two cameras to gaze at a single point in 3D space. However, this system may become unstable when the time response deteriorates by excessively interfering in a mutual control loop or by strict restriction of the camera action. In order to solve these problems, we introduce the concept of reliability into the K-means tracker, and propose a method for controlling the active cameras by using relative reliability. Less