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Modelling, optimization and design of visual sensor networks for sky surveillance
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University , 2013. , 210 p.
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 166
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-21022ISBN: 978-91-87557-11-8 (print)OAI: oai:DiVA.org:miun-21022DiVA: diva2:686670
Supervisors
Available from: 2014-01-13 Created: 2014-01-13 Last updated: 2014-04-24Bibliographically approved
List of papers
1. A taxonomy of visual surveillance systems
Open this publication in new window or tab >>A taxonomy of visual surveillance systems
2013 (English)Report (Other academic)
Abstract [en]

The increased security risk in society and the availability of low cost sensors and processors has expedited the research in surveillance systems. Visual surveillance systems provide real time monitoring of the environment. Designing an optimized surveillance system for a given application is a challenging task. Moreover, the choice of components for a given surveillance application out of a wide spectrum of available products is not an easy job.

 

In this report, we formulate a taxonomy to ease the design and classification of surveillance systems by combining their main features. The taxonomy is based on three main models: behavioral model, implementation model, and actuation model. The behavioral model helps to understand the behavior of a surveillance problem. The model is a set of functions such as detection, positioning, identification, tracking, and content handling. The behavioral model can be used to pinpoint the functions which are necessary for a particular situation. The implementation model structures the decisions which are necessary to implement the surveillance functions, recognized by the behavioral model. It is a set of constructs such as sensor type, node connectivity and node fixture. The actuation model is responsible for taking precautionary measures when a surveillance system detects some abnormal situation.

 

A number of surveillance systems are investigated and analyzed on the basis of developed taxonomy. The taxonomy is general enough to handle a vast range of surveillance systems. It has organized the core features of surveillance systems at one place. It may be considered an important tool when designing surveillance systems. The designers can use this tool to design surveillance systems with reduced effort, cost, and time.

Publisher
39 p.
Series
Research report in electronics
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-18859 (URN)STC (Local ID)978-91-87103-89-6 (ISBN)STC (Archive number)STC (OAI)
Available from: 2013-04-28 Created: 2013-04-28 Last updated: 2016-10-19Bibliographically approved
2. Solution space exploration of volumetric surveillance using a general taxonomy
Open this publication in new window or tab >>Solution space exploration of volumetric surveillance using a general taxonomy
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2013 (English)In: Proceedings of SPIE - The International Society for Optical Engineering / [ed] Daniel J. Henry, 2013, Art. no. 871317- p.Conference paper, Published paper (Refereed)
Abstract [en]

Visual surveillance systems provide real time monitoring of the events or the environment. The availability of low cost sensors and processors has increased the number of possible applications of these kinds of systems. However, designing an optimized visual surveillance system for a given application is a challenging task, which often becomes a unique design task for each system. Moreover, the choice of components for a given surveillance application out of a wide spectrum of available alternatives is not an easy job. In this paper, we propose to use a general surveillance taxonomy as a base to structure the analysis and development of surveillance systems. We demonstrate the proposed taxonomy for designing a volumetric surveillance system for monitoring the movement of eagles in wind parks aiming to avoid their collision with wind mills. The analysis of the problem is performed based on taxonomy and behavioral and implementation models are identified to formulate the solution space for the problem. Moreover, we show that there is a need for generalized volumetric optimization methods for camera deployment.

Series
Proceedings of SPIE, ISSN 0277-786X ; 8713
Keyword
Taxonomy; surveillance; coverage; visual sensor network
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-19280 (URN)10.1117/12.2022478 (DOI)000329624400038 ()2-s2.0-84881035758 (Scopus ID)STC (Local ID)978-081949504-4 (ISBN)STC (Archive number)STC (OAI)
Conference
Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications X; Baltimore, MD; United States; 1 May 2013 through 2 May 2013; Code 98146
Available from: 2013-06-18 Created: 2013-06-18 Last updated: 2016-10-19Bibliographically approved
3. Model and placement optimization of a sky surveillance visual sensor network
Open this publication in new window or tab >>Model and placement optimization of a sky surveillance visual sensor network
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2011 (English)In: Proceedings - 2011 International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2011, IEEE Computer Society, 2011, 357-362 p.Conference paper, Published paper (Refereed)
Abstract [en]

Visual Sensor Networks (VSNs) are networks which generate two dimensional data. The major difference between VSN and ordinary sensor network is the large amount of data. In VSN, a large number of camera nodes form a distributed system which can be deployed in many potential applications. In this paper we present a model of the physical parameters of a visual sensor network to track large birds, such as Golden Eagle, in the sky. The developed model is used to optimize the placement of the camera nodes in the VSN. A camera node is modeled as a function of its field of view, which is derived by the combination of the lens focal length and camera sensor. From the field of view and resolution of the sensor, a model for full coverage between two altitude limits has been developed. We show that the model can be used to minimize the number of sensor nodes for any given camera sensor, by exploring the focal lengths that both give full coverage and meet the minimum object size requirement. For the case of large bird surveillance we achieve 100% coverage for relevant altitudes using 20 camera nodes per km2 for the investigated camera sensors.

Place, publisher, year, edition, pages
IEEE Computer Society, 2011
Keyword
sensor networks, coverage, camera placement, surveillance, deployment optimization
National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-15663 (URN)10.1109/BWCCA.2011.56 (DOI)2-s2.0-84855849512 (Scopus ID)STC (Local ID)978-1-4577-1455-9 (ISBN)STC (Archive number)STC (OAI)
Conference
6th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA-2011;Barcelona, Catalonia;26 October 2011through28 October 2011;Category numberE4532;Code88069
Available from: 2012-01-05 Created: 2012-01-05 Last updated: 2016-10-19Bibliographically approved
4. Cost Optimization of a Sky Surveillance Visual Sensor Network
Open this publication in new window or tab >>Cost Optimization of a Sky Surveillance Visual Sensor Network
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2012 (English)In: Proceedings of SPIE - The International Society for Optical Engineering, Belgium: SPIE - International Society for Optical Engineering, 2012, Art. no. 84370U- p.Conference paper, Published paper (Refereed)
Abstract [en]

A Visual Sensor Network (VSN) is a network of spatially distributed cameras. The primary difference between VSN and other type of sensor network is the nature and volume of information. A VSN generally consists of cameras, communication, storage and central computer, where image data from multiple cameras is processed and fused. In this paper, we use optimization techniques to reduce the cost as derived by a model of a VSN to track large birds, such as Golden Eagle, in the sky. The core idea is to divide a given monitoring range of altitudes into a number of sub-ranges of altitudes. The sub-ranges of altitudes are monitored by individual VSNs, VSN1 monitors lower range, VSN2 monitors next higher and so on, such that a minimum cost is used to monitor a given area. The VSNs may use similar or different types of cameras but different optical components, thus, forming a heterogeneous network.  We have calculated the cost required to cover a given area by considering an altitudes range as single element and also by dividing it into sub-ranges. To cover a given area with given altitudes range, with a single VSN requires 694 camera nodes in comparison to dividing this range into sub-ranges of altitudes, which requires only 96 nodes, which is 86% reduction in the cost.

Place, publisher, year, edition, pages
Belgium: SPIE - International Society for Optical Engineering, 2012
Series
Proceedings of SPIE, ISSN 0277-786X ; 8437
Keyword
camera sensor networks, coverage, camera placement, surveillance, deployment optimization
National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-15669 (URN)10.1117/12.924344 (DOI)000305693900024 ()2-s2.0-84861946894 (Scopus ID)STC (Local ID)978-0-8194-9129-9 (ISBN)STC (Archive number)STC (OAI)
Conference
Real-Time Image and Video Processing 2012;Brussels;19 April 2012through19 April 2012;Code90041
Available from: 2012-01-05 Created: 2012-01-05 Last updated: 2016-10-19Bibliographically approved
5. Model, placement optimization and verification of a sky surveillance visual sensor network
Open this publication in new window or tab >>Model, placement optimization and verification of a sky surveillance visual sensor network
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2013 (English)In: International Journal of Space-Based and Situated Computing (IJSSC), ISSN 2044-4893, E-ISSN 2044-4907, Vol. 3, no 3, 125-135 p.Article in journal (Refereed) Published
Abstract [en]

A visual sensor network (VSN) is a distributed system of a large number of camera nodes, which generates two dimensional data. This paper presents a model of a VSN to track large birds, such as golden eagle, in the sky. The model optimises the placement of camera nodes in VSN. A camera node is modelled as a function of lens focal length and camera sensor. The VSN provides full coverage between two altitude limits. The model can be used to minimise the number of sensor nodes for any given camera sensor, by exploring the focal lengths that fulfils both the full coverage and minimum object size requirement. For the case of large bird surveillance, 100% coverage is achieved for relevant altitudes using 20 camera nodes per km² for the investigated camera sensors. A real VSN is designed and measurements of VSN parameters are performed. The results obtained verify the VSN model.

National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-17118 (URN)10.1504/IJSSC.2013.056380 (DOI)
Available from: 2012-10-02 Created: 2012-10-02 Last updated: 2017-05-04Bibliographically approved
6. Modeling and Verification of a Heterogeneous Sky Surveillance Visual Sensor Network
Open this publication in new window or tab >>Modeling and Verification of a Heterogeneous Sky Surveillance Visual Sensor Network
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2013 (English)In: International Journal of Distributed Sensor Networks, ISSN 1550-1329, E-ISSN 1550-1477, Art. id. 490489- p.Article in journal (Refereed) Published
Abstract [en]

A visual sensor network (VSN) is a distributed system of a large number of camera nodes and has useful applications in many areas. The primary difference between a VSN and an ordinary scalar sensor network is the nature and volume of the information. In contrast to scalar sensor networks, a VSN generates two-dimensional data in the form of images. In this paper, we design a heterogeneous VSN to reduce the implementation cost required for the surveillance of a given area between two altitude limits. The VSN is designed by combining three sub-VSNs, which results in a heterogeneous VSN. Measurements are performed to verify full coverage and minimum achieved object image resolution at the lower and higher altitudes, respectively, for each sub-VSN. Verification of the sub-VSNs also verifies the full coverage of the heterogeneous VSN, between the given altitudes limits. Results show that the heterogeneous VSN is very effective to decrease the implementation cost required for the coverage of a given area. More than 70% decrease in cost is achieved by using a heterogeneous VSN to cover a given area, in comparison to homogeneous VSN. © 2013 Naeem Ahmad et al.

National Category
Embedded Systems
Identifiers
urn:nbn:se:miun:diva-17121 (URN)10.1155/2013/490489 (DOI)000324191600001 ()2-s2.0-84884237155 (Scopus ID)STC (Local ID)STC (Archive number)STC (OAI)
Available from: 2012-10-02 Created: 2012-10-02 Last updated: 2016-10-19Bibliographically approved
7. Implementation of Wireless Vision Sensor Node With a Lightweight Bi-Level Video Coding
Open this publication in new window or tab >>Implementation of Wireless Vision Sensor Node With a Lightweight Bi-Level Video Coding
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2013 (English)In: IEEE Journal on Emerging and Selected Topics in Circuits and Systems, ISSN 2156-3357, Vol. 3, no 2, 198-209 p., 6508941Article in journal (Refereed) Published
Abstract [en]

Wireless vision sensor networks (WVSNs) consist ofa number of wireless vision sensor nodes (VSNs) which have limitedresources i.e., energy, memory, processing, and wireless bandwidth.The processing and communication energy requirements ofindividual VSN have been a challenge because of limited energyavailability. To meet this challenge, we have proposed and implementeda programmable and energy efficient VSN architecturewhich has lower energy requirements and has a reduced designcomplexity. In the proposed system, vision tasks are partitionedbetween the hardware implemented VSN and a server. The initialdata dominated tasks are implemented on the VSN while thecontrol dominated complex tasks are processed on a server. Thisstrategy will reduce both the processing energy consumption andthe design complexity. The communication energy consumption isreduced by implementing a lightweight bi-level video coding on theVSN. The energy consumption is measured on real hardware fordifferent applications and proposed VSN is compared against publishedsystems. The results show that, depending on the application,the energy consumption can be reduced by a factor of approximately1.5 up to 376 as compared to VSN without the bi-level videocoding. The proposed VSN offers energy efficient, generic architecturewith smaller design complexity on hardware reconfigurableplatform and offers easy adaptation for a number of applicationsas compared to published systems.

Place, publisher, year, edition, pages
IEEE Press, 2013
Keyword
Architecture, smart camera, video coding, wireless vision sensor networks (WVSNs), wireless vision sensor node (VSN)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-19193 (URN)10.1109/JETCAS.2013.2256816 (DOI)000337789200009 ()2-s2.0-84879076204 (Scopus ID)STC (Local ID)STC (Archive number)STC (OAI)
Available from: 2013-06-12 Created: 2013-06-12 Last updated: 2016-10-20Bibliographically approved

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