Fast vision-based minimum distance determination between known and unkown objects
Fast vision-based minimum distance determination between known and unkown objects
复制标题
DOI：
10.1109/iros.2007.4399208
复制
发表时间：
2007-12
期刊：
影响因子：
--
通讯作者：
Stefan Kuhn;D. Henrich
中科院分区：
文献类型：
--
作者：
Stefan Kuhn;D. Henrich
我们提出了一种方法，用于快速确定多个已知的和多个未知的对象之间的最小距离的相机图像。已知对象是具有已知几何形状、位置、方向和配置的对象。未知物体是必须由视觉传感器检测但具有未知几何形状、位置、取向和配置的物体。已知的物体被建模并在3D中扩展，然后投影到相机图像中。相机图像被分类为包括已知和未知对象的对象区域和非对象区域。通过搜索投影模型不与相机图像中被分类为未知的对象区域相交的最大扩展半径来保守地估计距离。该方法只需要最少的计算时间，可用于监控和安全应用。
We present a method for quickly determining the minimum distance between multiple known and multiple unkown objects within a camera image. Known objects are objects with known geometry, position, orientation, and configuration. Unkown objects are objects which have to be detected by a vision sensor but with unkown geometry, position, orientation and configuration. The known objects are modeled and expanded in 3D and then projected into a camera image. The camera image is classified into object areas including known and unknown objects and into non-object areas. The distance is conservatively estimated by searching for the largest expansion radius where the projected model does not intersect the object areas classified as unknown in the camera image. The method requires only minimal computation times and can be used for surveillance and safety applications.