Research on Three-Dimensional "High Presence Relay" System

A)在本研究中，我们提出了一种基于K-means聚类的目标跟踪算法（称为K-means跟踪器），该算法可以鲁棒地跟踪具有可以看到背景的孔洞的目标（例如蚊蝇）。本研究的贡献包括：1)使用5D特征向量统一表示每个像素的颜色“Y，U，V”和几何“x， Y”特征。这使得在跟踪过程中同时适应颜色和几何特征；2)利用变椭圆模型对搜索区域进行描述，并对周围背景进行建模。这保证了在各种几何变换下目标的稳定跟踪；3)实现“目标”和“背景”样本的自动自跟踪故障检测和恢复。此外，我们提出了一种基于可靠性的K-means聚类算法（称为RK-means跟踪器）来去除目标物体上的噪声背景像素（既不与目标相似，也不与背景相似）。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）赋予高可靠性值并正确分类，而将噪声像素赋予低可靠性值并忽略。为了提高算法的处理速度和鲁棒性，提出了径向采样方法。B)在本研究中，我们提出了一种高性能的目标跟踪系统，通过控制固定视点平移摄像机（FV-PTZ摄像机）以视频速率获得高速运动物体的高质量图像。为了实现我们的目标，我们使用K-means跟踪算法来跟踪由活动摄像机捕获的图像序列中的对象。我们利用k均值跟踪器的结果，采用PID控制方案来控制每个平移-倾斜-变焦单元的角度位置和速度。此外，通过使用两个摄像头，可以使用双目立体视觉算法获得物体的三维位置和速度。这些结果用于调整焦距和变焦。我们的系统允许两个摄像头凝视3D空间中的一个点。然而，当在相互控制回路中过度干扰或严格限制相机动作时，该系统可能会变得不稳定。为了解决这些问题，我们在k均值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对活动摄像机进行控制的方法。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