VISNET: A distributed vision testbed

Quinn, Max; Mudumbai, Raghuraman; Kuo, Thomas; Ni, Zefeng; Leo, Carter De; Manjunath, B.S.

doi:10.1109/icdsc.2008.4635707

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…Distributed tracking on smart camera systems has attracted a lot of recent attention and a number of groups have developed systems for this task [19,20,13,21,6,9,4]. For example Kayumbi, Anjum and Cavallaro [11] describe an effective scheme for localizing soccer players with a network of distributed cameras.…”

Section: Related Workmentioning

confidence: 99%

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Distributed target tracking using self localizing smart camera networks

Shirmohammadi

Taylor

2010

Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras

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This paper describes a novel decentralized target tracking scheme for distributed smart cameras. This approach is built on top of a distributed localization protocol which allows the smart camera nodes to automatically identify neighboring sensors with overlapping fields of regard and establish a communication graph which reflects how the nodes will interact to fuse measurements in the network. The new protocol distributes the detection and tracking problems evenly throughout the network accounting for sensor handoffs in a seamless manner. The approach also distributes knowledge about the state of tracked objects amongst the nodes in the network. This information can then be harvested through distributed queries which allow network participants to subscribe to different kinds of events that they may be interested in. The proposed scheme has been used to track targets in real time using a collection of custom designed smart camera nodes. Results from these experiments are presented.

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Section: Related Workmentioning

confidence: 99%

“…al. [19] propose a scheme for calibrating a set of cameras in a room and using them to track targets. Their approach splits the tracking task between a collection of smart camera nodes and a higher level process which fuses the sightings from these cameras.…”

Section: Related Workmentioning

confidence: 99%

Distributed target tracking using self localizing smart camera networks

Shirmohammadi

Taylor

2010

Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras

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“…The secondary camera utility is 1 for both 1 C and 2 C because they cover one common block, that is, Block (1,3).…”

Section: Secondary Global Utilitymentioning

confidence: 99%

“…A large number of research efforts have been made to explore efficient coordination between network cameras and maximize resource utilization [1][2][3][4][5][6][7][8]. One research topic of interest is to track targets with multiple allowable resolutions.…”

Section: Introductionmentioning

confidence: 99%

A game-theoretic design for collaborative tracking in a video camera network

Bhattacharya

2011

2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Tracking a moving target of interest at a high resolution with a dynamically designated network camera while ensuring complete-coverage of the area under surveillance of a video camera network can be a challenging task. Game theory can be applied to the situation, treating individual cameras as players and area coverage as utility. Camera collaboration is needed when one camera handoff the job of tracking a moving target of interest to another. By taking into account coverage shared by two cameras as secondary utility and giving precedence to border-covering camera directions, we can improve the performance of collaborative tracking, reducing complete-coverage failures as well as their associated uncovered blocks between handoffs. The cost of camera adjustment involved in a handoff can also be reduced this way.

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“…At the Robotic Vision Lab at Purdue University, 18 Cyclops smart cameras with Mica-Z motes form a network, which provides an overhead view of one room [10]. UC Santa Barbara has a 10 camera overlapping-view network called VISNET [12], which is used for camera calibration and selection research.…”

Section: Available Camera Networkmentioning

confidence: 99%

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

Kuo

Leo

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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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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VISNET: A distributed vision testbed

Cited by 7 publications

References 13 publications

Distributed target tracking using self localizing smart camera networks

Distributed target tracking using self localizing smart camera networks

A game-theoretic design for collaborative tracking in a video camera network

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

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