Calibrating Distributed Camera Networks

Devarajan, Dhanya; Cheng, Zhanwen; Radke, Richard J.

doi:10.1109/jproc.2008.928759

Cited by 66 publications

(39 citation statements)

References 78 publications

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“…More recently, several methods have been proposed for the calibration and localization of cameras in sensor networks [2], [5]. Each camera's focal length , location , and sensing direction can be estimated as shown in [5].…”

Section: A Sensing Modelmentioning

confidence: 99%

A Spatial Correlation Model for Visual Information in Wireless Multimedia Sensor Networks

Dai

Akyildiz

2009

IEEE Trans. Multimedia

113

117

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Abstract-Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists correlation among the visual information observed by cameras with overlapped field of views. This paper proposes a novel spatial correlation model for visual information in WMSNs. By studying the sensing model and deployments of cameras, a spatial correlation function is derived to describe the correlation characteristics of visual information observed by cameras with overlapped field of views. The joint effect of multiple correlated cameras is also studied. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. Furthermore, according to the proposed correlation function and entropy-based framework, a correlation-based camera selection algorithm is designed. Experimental results show that the proposed spatial correlation function can model the correlation characteristics of visual information in WMSNs through low computation and communication costs. Further simulations show that, given a distortion bound at the sink, the correlation-based camera selection algorithm requires fewer cameras to report to the sink than the random selection algorithm.

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Section: A Sensing Modelmentioning

confidence: 99%

A Spatial Correlation Model for Visual Information in Wireless Multimedia Sensor Networks

Dai

Akyildiz

2009

IEEE Trans. Multimedia

113

117

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“…The sensing process of a video sensor is characterized by 3-D to 2-D projection, for which the key parameter is the focal length (f ). Both the FoV parameters and the focal length could be estimated through calibration methods for distributed camera networks [8].…”

Section: A Correlation Of Visual Information In Sensor Networkmentioning

confidence: 99%

“…Based on this condition, and using the Chebyshev's inequality, we can derive the delay variance constraint in (8), where Δt q ij is the estimated delay variance of this hop.…”

Section: ) Local Delay Requirementsmentioning

confidence: 99%

Correlation-Aware QoS Routing for Wireless Video Sensor Networks

Dai

Wang

Akyildiz

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Abstract-The spatial correlation among the images retrived from distributed video sensors leads to considerable data redundancy, thus resulting in significant performance degradation in energy efficiency and QoS satisfaction. In this paper, a correlation-aware QoS routing algorithm (CAQR) is proposed to efficiently deliver visual information under QoS constraints by exploiting the correlation among video sensors. Firstly, a correlation-aware differential coding scheme is designed to reduce the amount of traffic generated by correlated video sensors. Then, a correlation-aware load balancing scheme is proposed to prevent network congestion by spliting the correlated flows that cannot be reduced to different paths. Finally, these correlationaware schemes are integrated into an optimization QoS routing framework with an objective to minimize energy consumption subject to QoS constraints. Simulation results show that the proposed algorithm achieves efficient delivery of visual information under QoS constraints in wireless video sensor networks.

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“…For example, we recently presented a distributed algorithm for the calibration of a multi-camera network that was designed with these considerations in mind [41]. We refer the reader to our recent survey on distributed computer vision algorithms [42] for more information.…”

Section: Further Resourcesmentioning

confidence: 99%

Multi-View Geometry for Camera Networks

Radke¹

2009

Multi-Camera Networks

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Designing computer vision algorithms for camera networks requires an understanding of how images of the same scene from different viewpoints are related. This chapter introduces the basics of multiview geometry in computer vision, including image formation and camera matrices, epipolar geometry and the fundamental matrix, projective transformations, and N -camera geometry. We also discuss feature detection and matching, and describe basic estimation algorithms for the most common problems that arise in multiview geometry.

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Calibrating Distributed Camera Networks

Cited by 66 publications

References 78 publications

A Spatial Correlation Model for Visual Information in Wireless Multimedia Sensor Networks

A Spatial Correlation Model for Visual Information in Wireless Multimedia Sensor Networks

Correlation-Aware QoS Routing for Wireless Video Sensor Networks

Multi-View Geometry for Camera Networks

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