Design and implementation of a wide area, large-scale camera network

Abstract

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.

Spatial-temporal understanding of urban scenes through large camera network

“…We collected our data from a recently deployed camera network at UCSB campus [7]. The network consists of over 40 stationary cameras covering wide area of the campus.…”

“…Here we demonstrate the potential of urban dynamic understanding through multiple cameras covering a large area. In particular, we collected videos from a network consisting of 27 cameras at the University of California at Santa Barbara (UCSB) [7] spanning a period of eight weeks. We show that better scene understanding can be achieved by analyzing the videos with simple vision techniques along with additional information of the area, such as campus class schedule.…”

Methodology for Large-Scale Camera Positioning to Enable Intelligent Self-Configuration

“…A system using the interaction of 30 cameras was set up by Wei et al for monitoring an area of 1670 m 2 [ 12 ]. Approximately 100 stationary cameras were integrated by Kuo et al [ 13 ], enabling a high degree of collaborative working among the cameras, for campus monitoring. Jiarui Lin et al presented a ceiling-mounted workshop measurement positioning system (C-wMPS), to compensate for many deficiencies shown by conventional metrology systems [ 14 ].…”