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Key Nodes Modeling for Object Detection and Location on Construction site using Color-Depth Cameras

机译:使用彩色摄像头在施工现场进行对象检测和定位的关键节点建模

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摘要

Object detection and tracking is a challenging problem in the dynamic construction environment, especially for objects with kinematic joints and changing poses, e.g. excavators. This paper presents a kinematic key nodes model for dynamically detecting and locating movable objects in construction sites using color-depth cameras. The key nodes model is designed by first analyzing kinematic constraints from mechanic specifications of interested construction site objects and then building representative segmented contour templates under supervision. Feature matching is realized by comparing the real object components and segmented contour templates in a traversal order of the tree structure for all the key nodes. Since color-depth cameras capture not only images, but also the distance between detected objects and the camera itself in a format of three-dimensional (3D) point clouds, the 3D spatial location of an object is obtained by linking the image coordinate of each detected key node with the 3D point coordinates. This proposed framework showed great potential in construction monitoring and safety management.
机译:在动态建筑环境中,物体的检测和跟踪是一个具有挑战性的问题,特别是对于具有运动学关节和不断变化的姿势(例如,运动姿态)的物体而言。挖掘机。本文提出了一种运动学关键节点模型,用于使用色深相机动态检测和定位建筑工地中的可移动物体。首先通过从感兴趣的建筑工地对象的力学规格分析运动学约束,然后在监督下构建有代表性的分段轮廓模板,设计关键节点模型。通过以树结构的遍历顺序比较所有关键节点的真实对象成分和分段轮廓模板来实现特征匹配。由于色深相机不仅可以捕获图像,而且还可以以三维(3D)点云格式捕获检测到的对象与相机本身之间的距离,因此,通过链接每个对象的图像坐标可以获得对象的3D空间位置检测到的具有3D点坐标的关键节点。拟议的框架显示了在施工监测和安全管理方面的巨大潜力。

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