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Study on an environmental embedding type "eye camera"

一种环境嵌入型“眼睛相机”的研究

基本信息

批准号：
16500112
负责人：
WU Haiyuan
金额：
$ 2.37万
依托单位：
Wakayama University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-16500112/
关键词：
Visual direction estimation Two-circle method Eve model Tracking the contour of the iris Case instance based learning Relative reliability Active camera control Stereo vision 黒目輪郭の追跡

项目摘要

In this research, we propose a sophisticated method to estimate visual direction using iris contours. This method requires only one monocular image taken by a camera with unknown focal length. In order to estimate the visual direction, we assume the visual directions of both eyes are parallel and iris boundaries are circles in 3D space. In this case, the two planes where the iris boundaries reside are also parallel. We estimate the normal vector of the two planes from the iris contours extracted from an input image by using an extended "two-circle" algorithm.In this research, we also propose a general object tracking method (called as "K-means tracker"), which is realized by classifying the pixels within the search area into "target" and "background" groups with a K-means clustering based algorithm. We first use a 5D feature vector to describe both the color ("Y,U,V") and the position ("x,y") of each pixel uniformly. This enables the simultaneous adaptation to both the color and geometric features during tracking. Secondly, we use a variable ellipse model to describe the shape of the search area and to model the surrounding background. This guarantees the stable object tracking under various geometric transformations.Moreover, we propose a high performance object tracking system for obtaining high quality images of a moving object at video rate by controlling an active camera mounted on a motor droved pan-tilt unit. We use "K-means tracker" for tracking object on image space. The PID control scheme is employed for controlling the angular position and speed of the pan-tilt units according to the results of the "K-means tracker". With this system, we can obtain motion-blur-free images of a moving object.

在这项研究中，我们提出了一种复杂的利用虹膜轮廓估计视觉方向的方法。这种方法只需要一台焦距未知的相机拍摄一幅单目图像。为了估计视觉方向，我们假设双眼的视觉方向是平行的，虹膜边界是三维空间中的圆形。在这种情况下，虹膜边界所在的两个平面也是平行的。提出了一种通用的目标跟踪方法--K-Means跟踪法，该方法通过K-Means聚类算法将搜索区域内的像素点分为“目标”组和“背景”组实现。我们首先使用一个5D特征向量来统一描述每个像素的颜色(“Y，U，V”)和位置(“x，y”)。这使得在跟踪过程中能够同时适应颜色和几何特征。其次，我们使用可变椭圆模型来描述搜索区域的形状，并对周围的背景进行建模。提出了一种高性能的目标跟踪系统，通过控制安装在电机驱动的云台上的主动摄像头，获得视频速率下运动目标的高质量图像。我们使用“K-均值跟踪器”来跟踪图像空间中的目标。根据“K均值跟踪器”的结果，采用了PID控制方案来控制云台的角位置和速度。利用该系统，我们可以获得运动物体的无运动模糊图像。