Online Control of Active Camera Networks for Computer Vision Tasks

被引:6
作者
Ilie, Adrian [1 ]
Welch, Greg [1 ,2 ,3 ]
机构
[1] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27515 USA
[2] Univ Cent Florida, Inst Simulat & Training, Orlando, FL 32816 USA
[3] Univ Cent Florida, Dept Elect Engn & Comp Sci, Orlando, FL 32816 USA
关键词
Algorithms; Experimentation; Measurement; Performance; Camera control; active camera networks; computer vision; surveillance; motion capture; 3D reconstruction; SURVEILLANCE;
D O I
10.1145/2530283
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Large networks of cameras have been increasingly employed to capture dynamic events for tasks such as surveillance and training. When using active cameras to capture events distributed throughout a large area, human control becomes impractical and unreliable. This has led to the development of automated approaches for online camera control. We introduce a new automated camera control approach that consists of a stochastic performancemetric and a constrained optimizationmethod. The metric quantifies the uncertainty in the state of multiple points on each target. It uses state-space methods with stochastic models of target dynamics and camera measurements. It can account for occlusions, accommodate requirements specific to the algorithms used to process the images, and incorporate other factors that can affect their results. The optimization explores the space of camera configurations over time under constraints associated with the cameras, the predicted target trajectories, and the image processing algorithms. The approach can be applied to conventional surveillance tasks (e. g., tracking or face recognition), as well as tasks employing more complex computer vision methods (e. g., markerless motion capture or 3D reconstruction).
引用
收藏
页数:40
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