Collaborative Sensing in a Distributed PTZ Camera Network

The performance of dynamic scene algorithms often suffers because of the inability to effectively acquire features on the targets, particularly when they are distributed over a wide field of view. In this paper, we propose an integrated analysis and control framework for a pan, tilt, zoom (PTZ) came...

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Vydané v:IEEE transactions on image processing Ročník 21; číslo 7; s. 3282 - 3295
Hlavní autori: Chong Ding, Bi Song, Morye, A., Farrell, J. A., Roy-Chowdhury, A. K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.07.2012
Institute of Electrical and Electronics Engineers
Predmet:
Applied sciences
Camera networks
Cameras
Collaboration
cooperative camera control
Detection, estimation, filtering, equalization, prediction
distributed estimation
Exact sciences and technology
game theory
Games
Image analysis
Image processing
Image resolution
Information, signal and communications theory
Optimization
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Target tracking
Telecommunications and information theory
video analysis
Performance evaluation
Autonomous system
video analysis
Optimization
Distributed processing
Accuracy
Covariance
s parameter
Zoom
Camera networks
Localization
Active network
Wide viewing angle
Signal detection
distributed estimation
Target tracking
Image resolution
Algorithm
Game theory
Image quality
Tilt angle
cooperative camera control
Image analysis
Simulation
Scene analysis
Sensor array
Personal communication networks
ISSN:1057-7149, 1941-0042, 1941-0042
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Popis
Shrnutí:The performance of dynamic scene algorithms often suffers because of the inability to effectively acquire features on the targets, particularly when they are distributed over a wide field of view. In this paper, we propose an integrated analysis and control framework for a pan, tilt, zoom (PTZ) camera network in order to maximize various scene understanding performance criteria (e.g., tracking accuracy, best shot, and image resolution) through dynamic camera-to-target assignment and efficient feature acquisition. Moreover, we consider the situation where processing is distributed across the network since it is often unrealistic to have all the image data at a central location. In such situations, the cameras, although autonomous, must collaborate among themselves because each camera's PTZ parameter entails constraints on the others. Motivated by recent work in cooperative control of sensor networks, we propose a distributed optimization strategy, which can be modeled as a game involving the cameras and targets. The cameras gain by reducing the error covariance of the tracked targets or through higher resolution feature acquisition, which, however, comes at the risk of losing the dynamic target. Through the optimization of this reward-versus-risk tradeoff, we are able to control the PTZ parameters of the cameras and assign them to targets dynamically. The tracks, upon which the control algorithm is dependent, are obtained through a consensus estimation algorithm whereby cameras can arrive at a consensus on the state of each target through a negotiation strategy. We analyze the performance of this collaborative sensing strategy in active camera networks in a simulation environment, as well as a real-life camera network.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2012.2188806