Approximating Optimal Visual Sensor Placement

Hörster, Eva; Lienhart, Rainer

doi:10.1109/icme.2006.262766

Cited by 73 publications

(52 citation statements)

References 3 publications

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“…The similar works can be found in [23], [25]. The authors of [25] addressed the problem on how to select the optimal number of camera sensors and determine their placement in a given monitored area for multimedia surveillance systems.…”

Section: Related Workmentioning

confidence: 93%

Localization-Oriented Coverage in Wireless Camera Sensor Networks

Liu

Zhang

2011

IEEE Trans. Wireless Commun.

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Abstract-In this paper, we investigate the coverage problem from the perspective of target localization for wireless camera sensor networks. We first propose a novel localization-oriented sensing model based on the perspective projection of camera sensors. Based on the sensing model, we propose a new notion of coverage, Localization-oriented coverage (L-coverage for short), by using Bayesian estimation theory. Furthermore, we analyze the relationship between the density of camera sensors and the L-coverage probability under random deployment where camera sensors are deployed according to a 2-dimensional Poisson process. According to the relationship between the density of camera sensors and the L-coverage probability, we derive the density requirements for an expected L-coverage probability. We validate and evaluate our proposed models and schemes by simulations.

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Section: Related Workmentioning

confidence: 93%

Localization-Oriented Coverage in Wireless Camera Sensor Networks

Liu

Zhang

2011

IEEE Trans. Wireless Commun.

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“…Horster and Lienhart [2006] have proposed an algorithm to minimize the cost of a visual sensor array while ensuring a proper coverage. For tracking applications, Chen and Davis [2000] have developed a resolution metric for camera placement considering occlusions.…”

Section: Related Workmentioning

confidence: 99%

Design of multimedia surveillance systems

Sivaram

Kankanhalli

Ramakrishnan

2009

ACM Trans. Multimedia Comput. Commun. Appl.

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This article addresses the problem of how to select the optimal combination of sensors and how to determine their optimal placement in a surveillance region in order to meet the given performance requirements at a minimal cost for a multimedia surveillance system. We propose to solve this problem by obtaining a performance vector, with its elements representing the performances of subtasks, for a given input combination of sensors and their placement. Then we show that the optimal sensor selection problem can be converted into the form of Integer Linear Programming problem (ILP) by using a linear model for computing the optimal performance vector corresponding to a sensor combination. Optimal performance vector corresponding to a sensor combination refers to the performance vector corresponding to the optimal placement of a sensor combination. To demonstrate the utility of our technique, we design and build a surveillance system consisting of PTZ (Pan-Tilt-Zoom) cameras and active motion sensors for capturing faces. Finally, we show experimentally that optimal placement of sensors based on the design maximizes the system performance.

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“…Space is represented as a occupancy grid and the authors focused on planar regions. A similar task is considered in [12] and also solved by linear programming techniques. In [19] the authors analyze the visibility from static sensors probabilistically and present a solution for maximizing visibility in a given region of interest.…”

Section: Introductionmentioning

confidence: 99%

Calibrating and optimizing poses of visual sensors in distributed platforms

Hörster

Lienhart

2006

Multimedia Systems

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Many novel multimedia, home entertainment, visual surveillance and health applications use multiple audio-visual sensors. We present a novel approach for position and pose calibration of visual sensors, i.e. cameras, in a distributed network of general purpose computing devices (GPCs). It complements our work on position calibration of audio sensors and actuators in a distributed computing platform [22]. The approach is suitable for a wide range of possible -even mobile -setups since (a) synchronization is not required, (b) it works automatically, (c) only weak restrictions are imposed on the positions of the cameras, and (d) no upper limit on the number of cameras and displays under calibration is imposed. Corresponding points across different camera images are established automatically. Cameras do not have to share one common view. Only a reasonable overlap between camera subgroups is necessary. The method has been sucessfully tested in numerous multi-camera environments with a varying number of cameras and has proven itself to work extremely accurate. Once all distributed visual sensors are calibrated, we focus on post-optimizing their poses to increase coverage of the space observed. A linear programming approach is derived that determines jointly for each camera the pan and tilt angle that maximizes the coverage of the space at a given sampling frequency. Experimental results clearly demonstrate the gain in visual coverage.

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Approximating Optimal Visual Sensor Placement

Cited by 73 publications

References 3 publications

Localization-Oriented Coverage in Wireless Camera Sensor Networks

Localization-Oriented Coverage in Wireless Camera Sensor Networks

Design of multimedia surveillance systems

Calibrating and optimizing poses of visual sensors in distributed platforms

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