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ACM Southeast Regional Conference

作者： Wang, Z. Brown, T. Shan, T. Wang, F. Xue, J. Univ Mississippi Sch Engn University MS 38677 USA Google Inc Mountain View CA USA

ISBN: (纸本)9781450350242

Extensive efforts have been devoted to achieve k-coverage for wireless sensor networks (WSNs) deployed in the sensing area by considering omni-directional sensing ranges as seen in scalar temperature sensors, where the larger k is, fault tolerance and monitoring quality is better. However, in wireless visual sensor networks (WVSNs), the sensing range of a visual sensor is often directional and modeled as a convex triangular shape in 2D and a pyramid-shape in 3D, which renders the k-coverage solutions proposed for WSNs incapable of solving the problem in WVSNs. Moreover, a number of WVSN applications consider 3D indoor space monitoring, which makes previous solutions proposed for 2D WVSN coverage less effective. In this paper, we take an initial step to tackle this challenging problem by considering 2-coverage for WVSNs in 3D indoor space monitoring. To maximize the information that can be captured by 2 visual sensors covering a 3D location, we propose to deploy the visual sensors from different directional angles and further extend 2-coverage to "2-angular-coverage" to denote such unique requirements in WVSNs. We demonstrate our simulated WVSN testing platform which emulates a 3D space and highlights our objective to provide efficient 2-angular-coverage in the indoor environment by minimizing the number of visual sensors required to cover the full regions through precision angle view monitoring. We formulate the general problem in a continuous space and then discretize our model via a lattice based approach. In addition, we also create a strategy to determine the degree of coverage for the WVSN, where a given location needs to be covered by at least 2 visual sensors that fulfill the angular-coverage requirement. We design an efficient greedy heuristic and an enhanced-Depth First Search algorithm to solve the discrete version problem, where the latter, if given enough time can return an optimal solution. By extensive simulations, we demonstrate that both our solution

关键词： visual sensor network k-coverage depth first search heuristic

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A visual sensor network for Parking Lot Occupancy Detection in Smart Cities 2

A Visual Sensor Network for Parking Lot Occupancy Detection ...

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2015 IEEE 2nd World Forum on Internet of Things (WF-IoT)

作者： Baroffio, Luca Bondi, Luca Cesana, Matteo Redondi, Alessandro Enrico Tagliasacchi, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781509003662

Technology is quickly revolutionizing our everyday lives, helping us to perform complex tasks. The Internet of Things (IoT) paradigm is getting more and more popular and is key to the development of Smart Cities. Among all the applications of IoT in the context of Smart Cities, realtime parking lot occupancy detection recently gained a lot of attention. Solutions based on computer vision yield good performance in terms of accuracy and are deployable on top of visual sensor networks. Since the problem of detecting vacant parking lots is usually distributed over multiple cameras, adhoc algorithms for content acquisition and transmission are to be devised. A traditional paradigm consists in acquiring and encoding images or videos and transmitting them to a central controller, which is responsible for analyzing such content. A novel paradigm, which moves part of the analysis to sensing devices, is quickly becoming popular. We propose a system for distributed parking lot occupancy detection based on the latter paradigm, showing that onboard analysis and transmission of simple features yield better performance with respect to the traditional paradigm in terms of the overall rate-energyaccuracy performance.

关键词： Parking Lot Occupancy Detection Smart Parking IoT visual sensor network visual Features

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A MULTI-AGENT ARCHITECTURE TO SUPPORT ACTIVE FUSION IN A visual sensor network

A MULTI-AGENT ARCHITECTURE TO SUPPORT ACTIVE FUSION IN A VIS...

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2nd ACM/IEEE International Conference on Distributed Smart Cameras

作者： Castanedo, Federico Garcia, Jesus Patricio, Miguel A. Molina, Jose M. Univ Carlos III Madrid Dept Comp Sci Appl Artificial Intelligence Grp E-28903 Getafe Spain

ISBN: (纸本)9781424426645

One of the main characteristics of a visual sensor network environment is the high amount of data generated. In addition, the application of some process, as for example tracking objects, generate a highly noisy output which may potentially produce an inconsistent system output. By inconsistent output we mean highly differences between tracking information provided by the visual sensors. A visual sensor network, with overlapped field of views, could exploit the redundancy between the field of view of each visual sensor to avoid inconsistencies and obtain more accurate results. In this paper, we present a visual sensor network system with overlapped field of views, modeled as a network of software agents. The communication of each software agent allows the use of feedback information in the visual sensors, called Active Fusion. Results of the software architecture to support Active Fusion scheme in an indoor scenario evaluation are presented.

关键词： visual sensor network Data Fusion Multi-Agent Systems

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Topological Mapping and Navigation Based on visual sensor network

Topological Mapping and Navigation Based on Visual Sensor Ne...

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11th IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Yuan, Wenbo Cao, Zhiqiang Tan, Min Liu, Jingyu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781479939794

In this paper, a novel topological mapping approach applied to the navigation mission is proposed based on visual sensor network. Firstly, the natural objects in the scene monitored by visual sensor network are recognized through the inference model based on structural features. Then, topological nodes are designed according to the recognized objects and their spatial structure information, and topological mapping will be fulfilled. On this basis, global localization for mobile robot in camera view can be implemented with HOG feature based detection and tracking. Finally, the robot can make motion decisions for navigation. Our approach does not rely on artificial landmarks and is tested in large-scale office scene. The effectiveness of the proposed approach is verified.

关键词： visual sensor network Topological mapping Navigation Mobile robot

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Multi-view Image Compression for visual sensor network based on Block Compressive Sensing and Multi-phase Joint Decoding

Multi-view Image Compression for Visual Sensor Network based...

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International Conference on Computational Science And Technology (ICCST)

作者： Ebrahim, Mansoor Chong, Chai Wai Sunway Univ Fac Sci & Technol 5 Jalan UnivBandar Sunway Petaling Jaya 46150 Selangor Malaysia

ISBN: (纸本)9781479932412

In this paper, a multi-view image compression framework for the visual sensor network (VSN) is proposed that involve the use of Block-based Compressive Sensing (BCS) and multi-phase joint decoding. In the proposed framework, one of the sensor nodes (encoder) is configured to serve as the reference node, whereas the others as non-reference nodes. The images captured by the reference and non-reference nodes are referred as I-R and I-NR respectively. They are encoded independently using the BCS to produce two measurements that will be transmitted to the host workstation (decoder). In this case, I-NR is always encoded at a lower bitrate when compared to I-R, because the idea is to improve the reconstruction of I-NR with the help of I-R. After the host workstation receives the two measurements, independent decoding is performed first, and then image registration is applied to project I-R onto the same plane of I-NR. The projected I-R is then fused with I-NR using wavelets. Subsequently, the difference between the measurement of the fused image and the measurement of I-NR is calculated. The difference is then decoded and added to I-f to produce the final improved version of I-NR. The simulation results show that the proposed framework is able to improve the quality of I-NR by 1dB to similar to 3dB at lower bitrates, when compared to the conventional BCS.

关键词： Compressive sensing multi-view image visual sensor network joint decoding

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Hardware-Friendly Laplacian-Based Multi-Focus Image Fusion in DCT Domain for visual sensor network

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IEEE ACCESS 2020年 8卷 165720-165739页

作者： Dhara, Sobhan Kanti Sen, Debashis Swamy, M. N. S. IIT Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

visual sensor network (VSN) requires a multi-focus image or video frame fusion technique involving focus measure computation in the DCT-domain to generate an all-in-focus image. Such techniques are implemented on resource-constrained on-board systems requiring hardware-friendly implementations. In this article, we first show that components of the Laplacian matrix are related to the discrete cosine transform (DCT) basis. The relation is that the eigenvalues of the Laplacian with proper boundary condition form the diagonal elements of the diagonal matrix generated by the DCT operation on the Laplacian. Exploiting this relation, we propose a focus measure which works on the DCT coefficients reflecting the spatial-domain Laplacian operation. Certain simplifications allow our focus measure computation through hardware-friendly integer multiplication and summation, where matrix multiplication involves just N scalar multiplications for an N x N 2D signal. Finally, we propose an approach which suitably fuses multi-focus images or video frames in DCT based image or video coding framework through detection of properly focused area and neighborhood consistency analysis. We show that our proposed approach is hardware-friendly, computationally simple, and is fast enough for VSN. Through experimental results, we show that our approach outperforms the relevant state-of-the-art in multi-focus image fusion for VSN both quantitatively and subjectively. We also show that our approach is effective in comparison to the state-of-the-art and a few latest generic multi-focus image fusion techniques in terms of quantitative and subjective evaluations.

关键词： Anti-diagonal matrix discrete cosine transform multi-focus image fusion visual sensor network

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Redundant node identification using Siamese neural network to enhance the availability in visual sensor network

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第3期37卷 e5655-e5655页

作者： Rajkumar, K. Paramasivan, B. Natl Engn Coll Dept CSE Kovilpatti 628503 Tamil Nadu India Natl Engn Coll Dept Informat Technol Kovilpatti Tamil Nadu India

Many monitoring applications related to surveillance, tracking and multipurpose visual monitoring have taken into consideration the use of wireless visual sensor networks. When sensors are deployed over a monitored field that could potentially damage the monitoring capability and availability in the visual sensor network. In order to overcome these issues, faulty nodes are identified and replaced by redundant node based on Siamese network in this research. Initially, camera nodes are randomly deployed in the visual sensor network, and the data are received from the network through gateway. To identify the redundant nodes, initially, the frames are divided into equivalent time slot, and then, Siamese network is utilized to identify the redundant nodes in a network. Siamese neural network is type of convolutional neural network that is utilized to recognize the similar images in the network. After that, faulty nodes are identified based on some parameters such as entropy, energy, transmission delay and network coverage. If the average energy, entropy, transmission delay and network coverage are below the threshold value, then the node is identified as faulty node. Finally, replace the faulty node with redundant node to enhance the availability in the visual sensor network for critical monitoring applications. The simulation analysis shows that the developed approach takes 772 s to identify redundant node and take 27 s to identify fake nodes and the developed method is executed at 1308 s. Thus, this prediction model helps to improve the coverage quality of target-based monitoring in order to achieve availability. Designing a Siamese network to identify redundant nodes to replace faulty nodes to improve the availability in visual sensor network. Identifying redundant nodes based on Siamese network to replace faulty nodes in the network. If average energy, entropy, transmission delay and network coverage are lesser than threshold value means, then the node is recognized

关键词： availability energy entropy fake node image similarity redundant node Siamese network visual sensor network

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A new image size reduction model for an efficient visual sensor network

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JOURNAL OF visual COMMUNICATION AND IMAGE REPRESENTATION 2019年 63卷 102573-000页

作者： Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Idris, Mohd Yamani Idna Anisi, Mohammad Hossein Blumenstein, Michael Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England UTS Sydney NSW Australia

Image size reduction for energy-efficient transmission without losing quality is critical in visual sensor networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime. (C) 2019 Elsevier Inc. All rights reserved.

关键词： visual sensor network Image size reduction Inter-redundancy Intra-redundancy Energy consumption Quality of the image

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Joint Prior Learning for visual sensor network Noisy Image Super-Resolution

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sensorS 2016年第3期16卷 288-288页

作者： Yue, Bo Wang, Shuang Liang, Xuefeng Jiao, Licheng Xu, Caijin Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Minist EducInt Res Ctr Intelligent Percept & Com Xian 710071 Peoples R China Kyoto Univ Grad Sch Informat Dept Intelligence Sci & Technol Kyoto 6068501 Japan

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. However, the captured images/videos are often low resolution with noise. Such visual data cannot be directly delivered to the advanced visual analysis. In this paper, we propose a joint-prior image super-resolution (JPISR) method using expectation maximization (EM) algorithm to improve VSN image quality. Unlike conventional methods that only focus on upscaling images, JPISR alternatively solves upscaling mapping and denoising in the E-step and M-step. To meet the requirement of the M-step, we introduce a novel non-local group-sparsity image filtering method to learn the explicit prior and induce the geometric duality between images to learn the implicit prior. The EM algorithm inherently combines the explicit prior and implicit prior by joint learning. Moreover, JPISR does not rely on large external datasets for training, which is much more practical in a VSN. Extensive experiments show that JPISR outperforms five state-of-the-art methods in terms of both PSNR, SSIM and visual perception.

关键词： visual sensor network image super-resolution prior learning EM algorithm image denoising

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Critical Location Spatial-Temporal Coverage Optimization in visual sensor network

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sensorS 2019年第19期19卷 4106-4106页

作者： Xiong, Yonghua Li, Jing Lu, Manjie China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

Coverage and network lifetime are two fundamental research issues in visual sensor networks. In some surveillance scenarios, there are some critical locations that demand to be monitored within a designated period. However, with limited sensor nodes resources, it may not be possible to meet both coverage and network lifetime requirements. Therefore, in order to satisfy the network lifetime constraint, sometimes the coverage needs to be traded for network lifetime. In this paper, we study how to schedule sensor nodes to maximize the spatial-temporal coverage of the critical locations under the constraint of network lifetime. First, we analyze the sensor node scheduling problem for the spatial-temporal coverage of the critical locations and establish a mathematical model of the node scheduling. Next, by analyzing the characteristics of the model, we propose a Two-phase Spatial-temporal Coverage-enhancing Method (TSCM). In phase one, a Particle Swarm Optimization (PSO) algorithm is employed to organize the directions of sensor nodes to maximize the number of covered critical locations. In the second phase, we apply a Genetic Algorithm (GA) to get the optimal working time sequence of each sensor node. New coding and decoding strategies are devised to make GA suitable for this scheduling problem. Finally, simulations are conducted and the results show that TSCM has better performance than other approaches.

关键词： visual sensor network spatial-temporal coverage sensor node scheduling two-phase coverage-enhancing method

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