Coverage estimation is one of the fundamental problems in sensornetworks. Coverage estimation in visual sensor networks (VSNs) is more challenging than in conventional 1-D (omnidirectional) scalar sensornetworks (SS...
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Coverage estimation is one of the fundamental problems in sensornetworks. Coverage estimation in visual sensor networks (VSNs) is more challenging than in conventional 1-D (omnidirectional) scalar sensornetworks (SSNs) because of the directional sensing nature of cameras and the existence of visual occlusion in crowded environments. This article represents a first attempt toward a closed-form solution for the visual coverage estimation problem in the presence of occlusions. We investigate a new target detection model, referred to as the certainty-based target detection (as compared to the traditional uncertainty-based target detection) to facilitate the formulation of the visual coverage problem. We then derive the closed-form solution for the estimation of the visual coverage probability based on this new target detection model that takes visual occlusions into account, According to the coverage estimation model, we further propose an estimate of the minimum sensor density that suffices to ensure a visual K-coverage in a crowded sensing field. Simulation is conducted which shows extreme consistency with results from theoretical formulation, especially when the boundary effect is considered. Thus, the closed-form solution for visual coverage estimation is effective when applied to real scenarios, such as efficient sensor deployment and optimal sleep scheduling.
In recent years, intelligent video surveillance attempts to provide content analysis tools to understand and predict the actions via video sensornetworks (VSN) for automated wide-area surveillance. In this emerging n...
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In recent years, intelligent video surveillance attempts to provide content analysis tools to understand and predict the actions via video sensornetworks (VSN) for automated wide-area surveillance. In this emerging network, visual object data is transmitted through different devices to adapt to the needs of the specific content analysis task. Therefore, they raise a new challenge for video delivery: how to efficiently transmit visual object data to various devices such as storage device, content analysis server, and remote client server through the network. Object-based video encoder can be used to reduce transmission bandwidth with minor quality loss. However, the involved motion-compensated technique often leads to high computational complexity and consequently increases the cost of VSN. In this paper, contextual redundancy associated with background and foreground objects in a scene is explored. A scene analysis method is proposed to classify macroblocks (MBs) by type of contextual redundancy. The motion search is only performed on the specific type of context of MB which really involves salient motion. To facilitate the encoding by context of MB, an improved object-based coding architecture, namely dual-closed-loop encoder, is derived. It encodes the classified context of MB in an operational rate-distortion-optimized sense. The experimental results show that the proposed coding framework can achieve higher coding efficiency than MPEG-4 coding and related object-based coding approaches, while significantly reducing coding complexity.
A visual sensor network (VSN) consists of a large amount of camera nodes which are able to process the captured image data locally and to extract the relevant information. The tight resource limitations in these netwo...
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A visual sensor network (VSN) consists of a large amount of camera nodes which are able to process the captured image data locally and to extract the relevant information. The tight resource limitations in these networks of embedded sensors and processors represent a major challenge for the application development. In this paper, we focus on finding optimal VSN configurations which are basically given by: 1) the selection of cameras to sufficiently monitor the area of interest;2) the setting of the cameras' frame rate and resolution to fulfill the quality of service requirements;and 3) the assignment of processing tasks to cameras to achieve all required monitoring activities. We formally specify this configuration problem and describe an efficient approximation method based on an evolutionary algorithm. We analyze our approximation method on three different scenarios and compare the predicted results with measurements on real implementations on a VSN platform. We finally combine our approximation method with an expectation-maximization algorithm for optimizing the coverage and resource allocation in VSN with pan-tilt-zoom camera nodes.
visual sensor networks (VSNs) have emerged as a new paradigm by giving sensors the capability to perceive and analyze their surroundings. Robust and fault-tolerant operations are crucial for non-contact plant monitori...
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visual sensor networks (VSNs) have emerged as a new paradigm by giving sensors the capability to perceive and analyze their surroundings. Robust and fault-tolerant operations are crucial for non-contact plant monitoring systems in a greenhouse environment equipped with a VSN. Achieving such capabilities requires new methods to monitor system status and crop health, and automatically reconfigure for new functionalities due to dynamic changes in the greenhouse environment. New technologies, such as field-programmable gate arrays (FPGAs), provide hardware designers with the ability to create high-performance and adaptive solutions. In this study, a distributed wireless sensor architecture with self-recovery capability was designed and constructed. The experimental results showed that the test bed comprised of wirelessly connected FPGAs (Xilinx Virtex 5) was able to achieve node-level fault detection and recovery within 2.16 s and network level recovery in 5.39 s. The application of the proposed architecture in greenhouse-based plant production is a significant step toward building a robust system for monitoring plant status.
We consider the problem of resource allocation for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor network (VSN). We use the Nash Bargaining Solution (NBS) from game theory in order to...
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ISBN:
(纸本)9781457705397
We consider the problem of resource allocation for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor network (VSN). We use the Nash Bargaining Solution (NBS) from game theory in order to determine the transmission power and source and channel coding rate for each node. The NBS assumes that the nodes negotiate (using the help of a centralized control unit) in order to jointly determine their transmission parameters. The transmission powers are allowed to take continuous values, whereas the source and channel coding rate combination can only assume discrete values. Thus, the resulting optimization problem is a mixed-integer optimization task and is solved using Particle Swarm Optimization (PSO). Experimental results are provided and conclusions are drawn.
In wireless visual sensor networks, the effect of transmission losses on the visual quality of images varies, depending on the length of burst *** the existing transmission error control techniques, interleaving can i...
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ISBN:
(纸本)9781617825057
In wireless visual sensor networks, the effect of transmission losses on the visual quality of images varies, depending on the length of burst *** the existing transmission error control techniques, interleaving can improve the visual quality of images without redundant *** the larger the interleaving data size is, the more effectively a longer burst loss is converted into isolated losses, which is at the cost of more pixels to be *** it is still an unavoidable issue that how to efficiently reduce individual sensor's data load for the purpose of energy-constrained distributed ***, an energy-aware interleaving algorithm is proposed in this paper, which regulates burst loss effects by spreading out packets according to each image region's precalculated transmission *** results demonstrate that, due to the balance of precalculated transmission income, the proposed scheme can not only improve the end-to-end image quality, but also prolong the lifetime of visual sensor network.
We propose a framework for hierarchical feature distribution scheme for object recognition in a network of visualsensors. The key of our approach is the principle that individual nodes in the network hold only a smal...
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ISBN:
(纸本)9781424446193
We propose a framework for hierarchical feature distribution scheme for object recognition in a network of visualsensors. The key of our approach is the principle that individual nodes in the network hold only a small amount of information about objects seen by the network, however, this small amount is sufficient to efficiently route queries through the network. Preliminary results suggest that amount of data transmitted through the network can be reduced in comparison to simpler feature distribution schemes.
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