Design and Implementation of a Wide Area, Large-Scale Camera Network

Thomas Kuo, Zefeng Ni, Carter De Leo, B.S. Manjunath
University of California, Santa Barbara
Department of Electrical and Computer Engineering, Santa Barbara, CA 93106
{thekuo,zefengni,cdeleo,manj} [at]


We describe a wide area camera network on a campus setting, the SCALLOPSNet (Scalable Large Optical Sensor Network). It covers with about 100 stationary cameras an expansive area that can be divided into three distinct regions: inside a building, along urban paths, and in a remote natural reserve. Some of these regions lack connections for power and communications, and, therefore, necessitate wireless, battery-powered camera nodes. In our exploration of available solutions, we found existing smart cameras to be insufficient for this task, and instead designed our own battery-powered camera nodes that communicate using 802.11b. The camera network uses the Internet Protocol on either wired or wireless networks to communicate with our central cluster, which runs cluster and cloud computing infrastructure. These frameworks like Apache Hadoop are well suited for large distributed and parallel tasks such as many computer vision algorithms. We discuss the design and implementation details of this network, together with the challenges faced in deploying such a large scale network on a research campus. We plan to make the datasets available for researchers in the computer vision community in the near future.
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Thomas Kuo, Zefeng Ni, Carter De Leo, and B.S. Manjunath,
IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Workshop on Camera Networks,, Jun. 2010.
Node ID: 550 , DB ID: 359 , Lab: VRL , Target: Workshop