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16th IEEE sensorS CONFERENCE

作者： Hendrikx, Leander De Poorter, Eli Munteanu, Adrian Vrije Univ Brussel IMEC ETRO Dept Brussels Belgium Univ Ghent IMEC IDLab Ghent Belgium

ISBN: (纸本)9781509010127

A novel approach for robot tracking and identification based on barcodes is proposed in this paper. The proposed system tracks robots fitted with barcodes identifying them. The system performs distributed visual processing and collaborative barcode tracking, whereby the nodes exchange processed visual information with appropriate neighboring nodes, informing them about incoming targets. The proposed tracking system is deployed on a visual sensor network (VSN) based on the Raspberry Pi. Its practical realization includes various components that handle real-time collaborative tracking, streaming, communication, configuration, management, monitoring, and deployment. The proposed system is able to efficiently and accurately track barcodes in real time. Complexity modeling demonstrates that the proposed distributed system brings substantial complexity reductions compared to a system of independently operating cameras. Experimental results demonstrate that the proposed system can track multiple barcodes in real time with an accuracy of less than 1 centimeter.

关键词： visual sensor networks Distributed processing Robot tracking Barcode detection

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Fast and accurate near-duplicate image elimination for visual sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED sensor networks 2017年第2期13卷 1-1页

作者： Zhou, Zhili Wu, Q. M. Jonathan Huang, Fang Sun, Xingming Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada

Currently, a huge amount of visual data such as digital images and videos have been collected by visual sensor nodes, that is, camera nodes, and distributed on visual sensor networks. Among the visual data, there are a lot of near-duplicate images, which cause a serious waste of limited storage, computing, and transmission resources of visual sensor networks and a negative impact on users' experience. Thus, near-duplicate image elimination is urgently demanded. This article proposes a fast and accurate near-duplicate elimination approach for visual sensor networks. First, a coarse-to-fine clustering method based on a combination of global feature and local feature is proposed to cluster near-duplicate images. Then in each near-duplicate group, we adopt PageRank algorithm to analyze the contextual relevance among images to select and reserve seed image and remove the others. The experimental results prove that the proposed approach achieves better performances in the aspects of both efficiency and accuracy compared with the state-of-the-art approaches.

关键词： visual sensor networks Internet of Things near-duplicate image elimination image copy detection near-duplicate detection

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Compress-then-Analyze versus Analyze-then-Compress: What Is Best in visual sensor networks?

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2016年第12期15卷 3000-3013页

作者： Redondi, Alessandro Baroffio, Luca Bianchi, Lucio Cesana, Matteo Tagliasacchi, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Pzza Leonardo da Vinci 32 I-20133 Milan Italy

visual sensor networks (VSNs) have attracted the interest of researchers worldwide in the last few years, and are expected to play a major role in the evolution of the Internet-of-Things (IoT). When used to perform visual analysis tasks, VSNs may be operated according to two different paradigms. In the traditional compress-then-analyze paradigm, images are acquired, compressed and transmitted for further analysis. Conversely, in the analyze-then-compress paradigm, image features are extracted by visual sensor nodes, encoded and then delivered to a remote destination where analysis is performed. The question this paper aims to answer is What is the best visual analysis paradigm in VSNs? To do this, first we empirically characterize the rate-energy-accuracy performance of the two aforementioned paradigms. Then, we leverage such models to formulate a resource allocation problem for VSNs. The problem optimally allocates the specific paradigm used by each camera node in the network and the related transmission source rate, with the objective of optimizing the accuracy of the visual analysis task and the VSN coverage. Experimental results over several VSNs instances demonstrate that there is no "winning" paradigm, but the best performance are obtained by allowing the coexistence of the two and by properly optimizing their utilization.

关键词： visual sensor networks local visual features resource allocation SIFT BRISK

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Characterization of Field-of-View for Energy Efficient Application-Aware visual sensor networks

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IEEE sensorS JOURNAL 2016年第9期16卷 3109-3122页

作者： Amjad, Anas Patwary, Mohammad Griffiths, Alison Soliman, Abdel-Hamid Staffordshire Univ Fac Comp Engn & Sci Stoke On Trent ST4 2DE Staffs England

Energy consumption is one of the primary concerns in a resource-constrained visual sensor network (VSN). The existing VSN design solutions under particular resource-constrained scenarios are application specific, whereas the degree of sensitivity of the resource constraints varies from one application to another. This limits the implementation of the existing energy efficient solutions within a VSN node, which may be considered to be a part of a heterogeneous network. The heterogeneity of image capture and processing within a VSN can be adaptively reflected with a dynamic field-of-view (FoV) realization. This is expected to allow the implementation of a generalized energy efficient solution to adapt with the heterogeneity of the network. In this paper, an energy efficient FoV characterization framework is proposed, which can support a diverse range of applications. The context of adaptivity in the proposed FoV characterization framework is considered to be: 1) sensing range selection;2) maximizing spatial coverage;3) adaptive task classification;and 4) minimizing the number of required nodes. Soft decision criteria is exploited, and it is observed that for a given detection reliability, the proposed framework provides energy efficient solutions, which can be implemented within heterogeneous networks. It is also found that the proposed design solution for heterogeneous networks leads to 49.8% energy savings compared with the trivial design solution.

关键词： Energy optimization field-of-view characteriation resource optimization sensing range estimation task classification visual sensor networks

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Prediction-Based Object Tracking in visual sensor networks

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WIRELESS PERSONAL COMMUNICATIONS 2016年第1期87卷 145-163页

作者： Chen, Tzung-Shi Tsai, Hua-Wen Peng, Jiun-Jie Natl Univ Tainan Dept Comp Sci & Informat Engn Tainan 700 Taiwan Lunghwa Univ Sci & Technol Dept Comp Informat & Network Engn Taoyuan 333 Taiwan Natl Univ Tainan Dept Informat & Learning Technol Tainan 700 Taiwan

This paper is to investigate the mobile object tracking in visual sensor networks. When visual sensors equipped with cameras are randomly deployed in a monitoring environment, many sensors are involved in covering the same mobile object. In a visual sensor network, images of the object may be captured by different sensors in different orientations simultaneously, and the captured images are then sent back to a base station or server. However, achieving full coverage for a set of selected characteristic points of an object invariably involves a great deal of redundant image data consuming the transmission energy for a visual sensor network. A novel approach is proposed to overcome this problem. The minimal number of sensors required for set coverage can be determined by predicting the direction and speed of the mobile object. Such sets are capable of covering the maximal number of characteristic points of view related to the mobile object at one time. The simulation results show that this approach reduces transmission cost while preserving the maximal coverage range of mobile objects.

关键词： Camera nodes Coverage Object tracking Prediction visual sensor networks

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Qol-Aware Unified Framework for Node Classification and Self-Reconfiguration Within Heterogeneous visual sensor networks

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IEEE ACCESS 2016年 4卷 9027-9042页

作者： Amjad, Anas Griffiths, Alison Patwary, Mohammad Staffordshire Univ Fac Comp Engn & Sci Stoke On Trent ST4 2DE Staffs England

Due to energy and throughput constraints of visual sensing nodes, in-node energy conservation is one of the prime concerns in visual sensor networks (VSNs) with wireless transceiving capability. To cope with these constraints, the energy efficiency of a VSN for a given level of reliability can be enhanced by reconfiguring its nodes dynamically to achieve optimal configurations. In this paper, a unified framework for node classification and dynamic self-reconfiguration in VSNs is proposed. The proposed framework incorporates quality-of-information (QoI) awareness using peak signal-to-noise ratio-based representative metric to support a diverse range of applications. First, for a given application, the proposed framework provides a feasible solution for the classification of visual sensing nodes based on their field-of-view by exploiting the heterogeneity of the targeted QoI within the sensing region. Second, with the dynamic realization of QoI, a strategy is devised for selecting suitable configurations of visual sensing nodes to reduce redundant visual content prior to transmission without sacrificing the expected information retrieval reliability. The robustness of the proposed framework is evaluated under various scenarios by considering: 1) target QoI thresholds;2) degree of heterogeneity;and 3) compression schemes. From the simulation results, it is observed that for the second degree of heterogeneity in targeted QoI, the unified framework outperforms its existing counterparts and results in up to 72% energy savings with as low as 94% reliability.

关键词： 3D field-of-view modelling dynamic reconfiguration energy optimization node classification quality-of-information reliability analysis visual sensor networks

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A Payoff-Based Learning Approach to Cooperative Environmental Monitoring for PTZ visual sensor networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2016年第3期61卷 709-724页

作者： Hatanaka, Takeshi Wasa, Yasuaki Funada, Riku Charalambides, Alexandros G. Fujita, Masayuki Tokyo Inst Technol Dept Mech & Control Engn Tokyo 1528550 Japan Cyprus Univ Technol Dept Environm Sci & Technol CY-3603 Lemesos Cyprus

This paper addresses cooperative environmental monitoring for Pan-Tilt-Zoom (PTZ) visual sensor networks. In particular, we investigate the optimal monitoring problem whose objective function value is intertwined with the uncertain state of the physical world. In addition, due to the large volume of vision data, it is desired for each sensor to execute processing through local computation and communication. To address these issues, we present a distributed solution to the problem based on game theoretic cooperative control and payoff-based learning. At the first stage, a utility function is designed so that the resulting game constitutes a potential game with potential function equal to the group objective function, where the designed utility is shown to be computable through local image processing and communication. Then, we present a payoff-based learning algorithm so that the sensors are led to the global objective function maximizers without using any prior information on the environmental state. Finally, we run experiments to demonstrate the effectiveness of the present approach.

关键词： Cyber-physical systems environmental monitoring game theoretic cooperative control payoff-based learning visual sensor networks

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Distributed barcode tracking in visual sensor networks

Distributed barcode tracking in visual sensor networks

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IEEE sensors

作者： Leander Hendrikx Eli De Poorter Adrian Munteanu Vrije Universiteit Brussel - imec ETRO Dept. Brussels Belgium Ghent University - imec IDLab Ghent Belgium

ISBN: (纸本)9781509010134

关键词： visual sensor networks Distributed processing Robot tracking Barcode detection Bar Codes sensor networks Distributed processing TRACKING SYSTEM tracking Robot Track visual processing

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Robot tracking in low-power visual sensor networks 16

Robot tracking in low-power visual sensor networks

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10th International Conference on Distributed Smart Cameras (ICDSC)

作者： Olti, Emoke Verbeke, Thomas Braeckman, Geert Dadarlat, Vasile Teodor Munteanu, Adrian TUCN Cluj Napoca Romania VUB Brussels Belgium

ISBN: (纸本)9781450347860

The paper addresses the problem of visual tracking in low-power visual sensor networks. Accurate real-time localization and energy efficiency are key issues in such applications. A novel approach for tracking and identification of robots using 1D barcodes is proposed. The nodes in the network perform visual processing and export processed information in form of robot codes and motion vectors. The central server can turn on/off processing on either node based on the received information. Furthermore, a novel LoRa-based low-power communication protocol is designed to provide the necessary bandwidth. The proposed system yields optimized node energy consumption, which we model mathematically and compare against the energy consumption of a system of independent cameras. The experimental results prove the correctness of the formulated model and demonstrate the advantages of the proposed approach. The performance of the system is practically demonstrated on LoRa-enabled Raspberry Pi cameras.

关键词： visual sensor networks Robot tracking LoRa networks

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Video Selection for visual sensor networks: a Motion-Based Ranking Algorithm 24

Video Selection for Visual Sensor Networks: a Motion-Based R...

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24th European Signal Processing Conference (EUSIPCO)

作者： Moretti, Simone Mazzotti, Matteo Chiani, Marco Univ Bologna DEI I-40126 Bologna Italy

ISBN: (纸本)9780992862657

A visual sensor Network (VSN) is composed by several cameras, in general with different characteristics and orientations, which are used to cover a certain Area of Interest (AoI). To provide an optimal and autonomous exploitation of the VSN video streams, suitable algorithms are needed for selecting the cameras capable to guarantee the best video quality for the specific AoI in the scene. In this work, a novel content and context-aware camera ranking algorithm is proposed, with the goal to maximize the Quality of Experience (QoE) to the final user. The proposed algorithm takes into account the pose, camera resolution and frame rate, and the quantity of motion in the scene. Subjective tests are performed to compare the ranking of the algorithm with human ranking. Finally, the proposed ranking algorithm is compared with common objective video quality metrics and a previous ranking algorithm, confirming the validity of the approach.

关键词： visual sensor networks QoE camera selection techniques ranking algorithms

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