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IEEE Latin-American Conference on Communications

作者： Garay, Jorge R. B. Kofuji, Sergio T. Tiba, Tadayoshi Univ Sao Paulo Integrated Syst Lab BR-05508970 Sao Paulo Brazil MRL Sao Paulo Brazil

ISBN: (纸本)9781424443871

This paper presents the proposal of integrating an Automatic Meter Reading System (AMR) with Computer Vision and Wireless Communication. The AMR system reads the numeral meter through a video micro-camera, process the collected image using an Optical Character Recognition (OCR) module embedded in the sensor node. The results are sent through ZigBee wireless links to the center of management, in which a mesh topology is used for communication.

关键词： AMR visual sensor networks OCR ZigBee Mesh topology

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A visual sensor Network for Object Recognition: Testbed Realization

A Visual Sensor Network for Object Recognition: Testbed Real...

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18th International Conference on Digital Signal Processing (DSP)

作者： Redondi, A. Baroffio, L. Canclini, A. Cesana, M. Tagliasacchi, M. Politecn Milan Dipartimento Elettron & Informaz I-20133 Milan Italy

ISBN: (纸本)9781467358057

This work describes the implementation of an object recognition service on top of energy and resource-constrained hardware. A complete pipeline for object recognition based on the BRISK visual features is implemented on Intel Imote2 sensor devices. The reference implementation is used to assess the performance of the object recognition pipeline in terms of processing time and recognition accuracy.

关键词： visual sensor networks object recognition feature extraction

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sensor-level Security and Privacy Protection by embedding Video Content Analysis

Sensor-level Security and Privacy Protection by embedding Vi...

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18th International Conference on Digital Signal Processing (DSP)

作者： Winkler, Thomas Rinner, Bernhard Alpen Adria Univ Klagenfurt Inst Networked & Embedded Syst A-9020 Klagenfurt Austria

ISBN: (纸本)9781467358057

In traditional video camera networks privacy protection is performed, if at all, at the data center after the video has been streamed over the network. With the on-board processing capabilities of today's visual sensor network devices, image processing and privacy protection can be performed now on the cameras before any data is sent over the network. While moving privacy protection closer to the data source is a big step forward, it raises also new challenges. The growing software stack of today's embedded devices and the widespread use of public wired and wireless network infrastructure entail substantial effort to secure this new class of devices. In this position paper we describe a novel approach that aims to tightly integrate security and privacy protection with the image sensor itself and thereby eliminating many security issues of today's embedded camera systems. Resource-efficient, online video content analysis plays a vital role in this endeavor towards making privacy protection an inherent property of future image acquisition units. This paper outlines the requirements and challenges of such an approach and discusses potential implementation strategies.

关键词： security privacy visual sensor networks

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CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING 22

CODING MODE DECISION ALGORITHM FOR BINARY DESCRIPTOR CODING

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22nd European Signal Processing Conference (EUSIPCO)

作者： Monteiro, Pedro Ascenso, Joao Inst Telecomunicacoes Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9780992862619

In visual sensor networks, local feature descriptors can be computed at the sensing nodes, which work collaboratively on the data obtained to make an efficient visual analysis. In fact, with a minimal amount of computational effort, the detection and extraction of local features, such as binary descriptors, can provide a reliable and compact image representation. In this paper, it is proposed to extract and code binary descriptors to meet the energy and bandwidth constraints at each sensing node. The major contribution is a binary descriptor coding technique that exploits the correlation using two different coding modes: Intra, which exploits the correlation between the elements that compose a descriptor;and Inter, which exploits the correlation between descriptors of the same image. The experimental results show bitrate savings up to 35% without any impact in the performance efficiency of the image retrieval task.

关键词： Local features feature coding binary descriptors mode decision visual sensor networks

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GREENEYES: NETWORKED ENERGY-AWARE visual ANALYSIS

GREENEYES: NETWORKED ENERGY-AWARE VISUAL ANALYSIS

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IEEE International Conference on Multimedia & Expo Workshops (ICMEW)

作者： Baroffio, L. Cesana, M. Redondi, A. Tagliasacchi, M. Ascenso, J. Monteiro, P. Eriksson, E. Dan, G. Fodor, V. Politecn Milan DEIB Milan Italy Royal Inst Technol KTH Stockholm Sweden Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9781479970797

The GreenEyes project aims at developing a comprehensive set of new methodologies, practical algorithms and protocols, to empower wireless sensor networks with vision capabilities. The key tenet of this research is that most visual analysis tasks can be carried out based on a succinct representation of the image, which entails both global and local features, while it disregards the underlying pixel-level representation. Specifically, GreenEyes will pursue the following goals: i) energy-constrained extraction of visual features;ii) rate-efficiency modelling and coding of visual feature;iii) networking streams of visual features. This will have a significant impact on several scenarios including, e.g., smart cities and environmental monitoring.

关键词： visual sensor networks Local visual Features visual Analysis

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Sleep Analysis for Elderly Care Using a Low-Resolution visual sensor Network 6th

Sleep Analysis for Elderly Care Using a Low-Resolution Visua...

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6th International Workshop on Human Behavior Understanding (HBU)

作者： Eldib, Mohamed Deboeverie, Francis Philips, Wilfried Aghajan, Hamid Univ Ghent TELIN IPI IMINDS B-9000 Ghent Belgium Stanford Univ Dept Elect Engn Palo Alto CA 94304 USA

ISBN: (纸本)9783319241951;9783319241944

Nearly half of the senior citizens report difficulty initiating and maintaining sleep. Frequent visits to the bathroom in the middle of the night is considered as one of the major reasons for sleep disorder. This leads to serious diseases such as depression and diabetes. In this paper, we propose to use a network of cheap low-resolution visual sensors (30 x 30 pixels) for long-term activity analysis of a senior citizen in a service flat. The main focus of our research is on elderly behaviour analysis to detect health deterioration. Specifically, this paper treats the analysis of sleep patterns. Firstly, motion patterns are detected. Then, a rule-based approach on the motion patterns is proposed to determine the wake up time and sleep time. The nightly bathroom visit is identified using a classification-based model. In our evaluation, we performed experiments on 10 months of real-life data. The ground truth is collected from the diaries in which the senior citizen wrote down his sleep time and wake up time. The results show accurate extraction of the sleep durations with an overall Mean Absolute Error (MAE) of 22.91 min and Spearman correlation coefficient of 0.69. Finally, the nightly bathroom visits analysis indicate sleep disorder in several nights.

关键词： Sleep disorder Sleep analysis visual sensor networks Ambient assisted living

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The ICSI M2M Middleware for IoT-based Intelligent Transportation Systems 18

The ICSI M2M Middleware for IoT-based Intelligent Transporta...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Azzara, Andrea Petracca, Matteo Pagano, Paolo Scuola Super St Anna Pisa Inst Commun Informat & Percept Technol Pisa Italy Consorzio Nazl Interuniv Telecomunicaz Natl Lab Photon Networks Pisa Italy

ISBN: (纸本)9781467365963

The Internet of Things is expected to play a fundamental role in a wide range of application fields. Intelligent Transportation Systems are among the most promising application domains, being inherently large-scale and composed by heterogeneous systems. In this paper we present the ICSI M2M Middleware, a software capable of supporting standard Machine-to-Machine communication, while tackling with the dynamic nature of resource-constrained devices and networks. The RESTFUL interface of the ICSI M2M Middleware provides a way to dynamically reconfigure distributed sensing applications, while the support for in-network aggregation guarantees an efficient utilization of network resources. Preliminary experimental results gathered through a laboratory testbed indicate that the ICSI M2M Middleware is a suitable solution for the ITS use-case of a visual sensor network of resource-constrained devices.

关键词： Internet of Things intelligent transportation systems middleware ICSI M2M middleware IoT-based intelligent transportation systems RESTful interface distributed sensing applications machine-to-machine communication machine-to-machine middleware network resource utilization visual sensor networks Libraries Logic gates Middleware Protocols Standards Wireless sensor networks XML Middleware Internet of things Intelligent transportation systems Logic gates machine to machine sensor networks extensible markup language Wireless sensor networks protocols Libraries Web resources Intracytoplasmic Sperm Injections Resource utilization

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Energy Aware Multi-Object Detection Method in visual sensor Network 5

Energy Aware Multi-Object Detection Method in Visual Sensor ...

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5th International Conference on Computer and Knowledge Engineering (lCCKE)

作者： Yousefi, Shamim Aghdasi, Hadi S. Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran

ISBN: (纸本)9781467392808

Nowadays, multi-object detection is one of the most important research issues in visual sensor networks. According to literature, many methods have been proposed for object detection. However, in most of the traditional works, preprocessing methods in camera nodes cause network to be faced with large amount of data when there are several objects in camera node field-of-views. In other words, conventional works can be suitable for single object detection. Therefore, in this paper, we propose an efficient preprocessing method in camera nodes for multi-object detection which is called Bounding box Boosting-based Face Detection (BBOD). In this method, first off, a background subtraction technique is used. Then the rows and columns of the objects bounding box are determined by a number of consecutive differences. After that, the bounding box of the all objects delivers to the Boosting-based face detection algorithm for extracting the faces of objects, and their boundaries. Finally, the detected faces are sent to the base station. The simulation results demonstrate that BBOD method injects less traffic to the network and saves camera nodes energy. Consequently, BBOD method increases the network lifetime in comparison with state-of-the-art algorithms.

关键词： visual sensor networks Multi-object detection Boosting-based face detection algorithm Energy efficiency Bounding box

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Multi-focus image fusion using Singular Value Decomposition in DCT domain 10

Multi-focus image fusion using Singular Value Decomposition ...

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10th Iranian Conference on Machine Vision and Image Processing (MVIP)

作者： Amin-Naji, Mostafa Ranjbar-Noiey, Pardis Aghagolzadeh, Ali Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol Sar Iran

ISBN: (纸本)9781538644058

Multi-focus image fusion is a process that fuses several images from a scene with different focal lengths into a whole image in which all areas are focused on. Image fusion methods in the Discrete Cosine Transform (DCT) domain are efficient due to their low time and energy consumption, and low complexity. This is especially true when fusing images are compressed in JPEG format in visual sensor networks (VSN). In this paper, a low complexity multi-focus image fusion in DCT domain is presented which increases the output image quality. Our proposed method makes it suitable for real-time applications because of its implementation in DCT domain. On the other hand, it is stable in noisy conditions. The proposed method uses the singular values of Singular Value Decomposition (SVD) of 8x8 input blocks in DCT domain. The geometric mean of the 5 largest singular values (out of 8 singular values) is computed as a criterion of focused block detection. The blocks which have the highest geometric mean value among other corresponding blocks is selected as the focused block. These blocks are then used for constructing the output image. This method can be utilized both in DCT domain and in spatial domain. Various experiments and comparisons between the proposed method and the previous methods in noisy and noiseless conditions have been presented, which confirm the increase in image quality and stability in noisy images.

关键词： multi-focus image fusion discrete cosine transform singular value decomposition visual sensor networks

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visual sensor network stimulation model identification via Gaussian mixture model and deep embedded features

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期114卷

作者： Varotto, Luca Fabris, Marco Michieletto, Giulia Cenedese, Angelo Univ Padua Dept Informat Engn Via Gradenigo 6-B I-35131 Padua Italy Univ Padua Dept Management & Engn Stradella San Nicola 3 I-36100 Vicenza Italy

visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring in quite active lines of research. An important research direction consists in the identification and estimation of the VSN sensing features: these are practically useful when scaling with the number of cameras or with the observed scene complexity. With this context in mind, this paper introduces for the first time the idea of Stimulation Model (SM), as a mathematical relation between the set of detectable events and the corresponding stimulated cameras observing those events. The formulation of the related SM identification problem is proposed, along with a proper network observations model, and a solution approach based on deep embedded features and soft clustering. In detail: first, the Gaussian Mixture Modeling is employed to provide a suitable description for data distribution, while an autoencoder is used to reduce undesired effects due to the so-called curse of dimensionality emerging in case of large scale networks. Then, it is shown that a SM can be learnt by solving Maximum A-Posteriori estimation on the encoded features belonging to a space with lower dimensionality. Numerical results on synthetic scenarios are reported to validate the devised estimation algorithm.

关键词： visual sensor networks Gaussian mixture model Dimensionality reduction Feature embedding

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