Distributed metric calibration of ad hoc camera networks

Devarajan, Dhanya; Radke, Richard J.; Chung, Haeyong

doi:10.1145/1167935.1167939

Cited by 89 publications

(65 citation statements)

References 44 publications

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“…In the rest of this section, we summarize our basic approach to the distributed calibration of camera networks, which is more fully described in [8], [9], [66]. Our goal is to design a calibration system in which each camera only communicates with (and possesses knowledge about) those cameras connected to it by an edge in the vision graph.…”

Section: Feature Matching and Vision Graph Edge Formationmentioning

confidence: 99%

“…In Section IV we review approaches for distributed estimation of the calibration parameters of all cameras in the network based on the vision graph. We focus on an efficient method for initializing and refining the calibration estimates we recently proposed in [8], [9]. Throughout Sections III and IV, we illustrate our results on a running example of a set of images acquired from cameras distributed throughout RPI's campus.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Calibrating Distributed Camera Networks

Devarajan¹,

Cheng²,

Radke

2008

Proc. IEEE

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Recent developments in wireless sensor networks have made feasible distributed camera networks, in which cameras and processing nodes may be spread over a wide geographical area, with no centralized processor and limited ability to communicate a large amount of information over long distances. This paper overviews distributed algorithms for the calibration of such camera networks-that is, the automatic estimation of each camera's position, orientation, and focal length. In particular, we discuss a decentralized method for obtaining the vision graph for a distributed camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. We next describe a distributed algorithm in which each camera performs a local, robust nonlinear optimization over the camera parameters and scene points of its vision graph neighbors to obtain an initial calibration estimate.We then show how a distributed inference algorithm based on belief propagation can refine the initial estimate to be both accurate and globally consistent.

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Section: Feature Matching and Vision Graph Edge Formationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibrating Distributed Camera Networks

Devarajan¹,

Cheng²,

Radke

2008

Proc. IEEE

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“…We assume that in the first phase of system operations, all cameras with overlapped FoVs are jointly calibrated [4], [9]. Because cameras monitor the scene, which is in one plane, we simplify the problem of volumetric coverage, and consider the coverage of the scene that lays on the parallel plane π 1 .…”

Section: Telepresence Application For Video-based Wireless Sensormentioning

confidence: 99%

On the coverage problem in video-based wireless sensor networks

Soro

Heinzelman

2005

2nd International Conference on Broadband Networks, 2005.

117

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-Video-based wireless sensor networks continue to gain increasing interest due to their ability to collect visual information for a wide range of applications. However, knowledge about these types of networks is mostly related to visual algorithms, leaving the networking perspective aside. In this work, we analyze how an algorithm designed for traditional wireless sensor networks, which integrates the coverage and routing problem, behaves in video-based networks. Our results show that because of the unique way that cameras capture data, the sensor network algorithm does not give the expected results in terms of coverage preservation of monitored areas. We discuss the main differences between traditional wireless sensor networks and video-based networks that lead to such a result, and we provide ideas for how protocols should be designed for the unique features of video-based networks.

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“…This has motivated several distributed localization algorithms in the camera sensor networks community such as [12], [13], [14], [15], [16], [17], [18], [19]. Semi-automatic camera network calibration methods use laser printed textures mounted on a board [12] or modulated LED emissions [13], [14].…”

Section: Introductionmentioning

confidence: 99%

“…Semi-automatic camera network calibration methods use laser printed textures mounted on a board [12] or modulated LED emissions [13], [14]. Automatic methods perform local SfM via robust bundle adjustment [17] and integrate the information using belief propagation [18]. However, these algorithms integrate the information in an ad-hoc fashion and do not provide rigorous proofs of convergence to the centralized solution.…”

Section: Introductionmentioning

confidence: 99%

Distributed calibration of Camera Sensor Networks

Elhamifar

Vidal

2009

2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC)

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Abstract-We propose a distributed algorithm for calibrating the intrinsic and extrinsic parameters of a Camera Sensor Network (CSN). We assume that only one of the cameras is calibrated and that the network graph, i.e. the graph over which the cameras communicate, is connected. Each camera uses simple algorithms based on epipolar geometry to obtain its calibration matrix as well as its pose relative to a reference frame. A distributed consensus algorithm is derived to enforce a globally consistent solution for the recovered 3D structure across the entire network. We demonstrate the validity and effectiveness of our method through synthetic experiments.

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Distributed metric calibration of ad hoc camera networks

Cited by 89 publications

References 44 publications

Calibrating Distributed Camera Networks

Calibrating Distributed Camera Networks

On the coverage problem in video-based wireless sensor networks

Distributed calibration of Camera Sensor Networks

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