Determining Vision Graphs for Distributed Camera Networks Using Feature Digests
Determining Vision Graphs for Distributed Camera Networks Using Feature Digests
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DOI：
10.1155/2007/57034
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发表时间：
2007
影响因子：
1.9
通讯作者：
Z. Cheng;D. Devarajan;R. Radke
中科院分区：
文献类型：
--
作者：
Z. Cheng;D. Devarajan;R. Radke
我们提出了一种去中心化方法，用于获取分布式自组织摄像机网络的视觉图，其中图的每条边代表两个摄像机，它们对同一环境的足够大的部分进行成像。每个摄像机将一组空间分布良好的独特的、近似视点不变的特征点编码成固定长度的“特征摘要”，并在整个网络中广播。每个接收器相机将其自身的特征与解压缩的摘要稳健地匹配，并决定是否存在足够的证据来形成视觉图边缘。我们还展示了仅沿视觉图边缘传递消息的相机校准算法如何以分布式方式恢复准确的 3D 结构和相机位置。我们分析了不同消息形成方案的性能，并表明使用模拟 60 节点室外摄像机网络可以实现高检测率 ()，同时保持低误报率 ()。
We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length "feature digest" that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates () can be achieved while maintaining low false alarm rates () using a simulated 60-node outdoor camera network.