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Predictive Distributed Visual Analysis for Video in Wireless Sensor Networks
KTH, School of Electrical Engineering (EES), Communication Networks.ORCID iD: 0000-0001-8968-3976
KTH, School of Electrical Engineering (EES), Communication Networks.ORCID iD: 0000-0002-4876-0223
KTH, School of Electrical Engineering (EES), Communication Networks.ORCID iD: 0000-0002-2764-8099
2016 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 15, no 7, 1743-1756 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

We consider the problem of performing distributed visual analysis for a video sequence in a visual sensor network that contains sensor nodes dedicated to processing. Visual analysis requires the detection and extraction of visual features from the images, and thus the time to complete the analysis depends on the number and on the spatial distribution of the features, both of which are unknown before performing the detection. In this paper, we formulate the minimization of the time needed to complete the distributed visual analysis for a video sequence subject to a mean average precision requirement as a stochastic optimization problem. We propose a solution based on two composite predictors that reconstruct randomly missing data, on quantile-based linear approximation of feature distribution and on time series analysis methods. The composite predictors allow us to compute an approximate optimal solution through linear programming. We use two surveillance video traces to evaluate the proposed algorithms, and show that prediction is essential for minimizing the completion time, even if the wireless channel conditions vary and introduce significant randomness. The results show that the last value predictor together with regular quantile-based distribution approximation provide a low complexity solution with very good performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. Vol. 15, no 7, 1743-1756 p.
Keyword [en]
Image analysis, wireless sensor networks, scheduling, distributed computation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-189919DOI: 10.1109/TMC.2015.2465390ISI: 000378499000012Scopus ID: 2-s2.0-84976566064OAI: oai:DiVA.org:kth-189919DiVA: diva2:950359
Note

QC 20160729

Available from: 2016-07-29 Created: 2016-07-25 Last updated: 2017-05-15Bibliographically approved
In thesis
1. Distributed Processing of Visual Features in Wireless Sensor Networks
Open this publication in new window or tab >>Distributed Processing of Visual Features in Wireless Sensor Networks
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

As digital cameras are becoming both cheaper and more advanced, they are also becoming more common both as part of hand-held and consumer devices, and as dedicated surveillance devices. The still images and videos collected by these cameras can be used as input to computer vision algorithms for performing tracking, scene understanding, navigation, etc. The performance of such computer vision tasks can be improved by having multiple cameras observing the same events. However, large scale deployment of camera networks is difficult in areas without access to infrastructure for providing power and network connectivity. In this thesis we consider the use of a network of camera equipped sensor nodes as a cost efficient alternative to conventional camera networks. To overcome the computational limitations of the sensor nodes, we enhance the sensor network with dedicated processing nodes, and process images in parallel using multiple processing nodes.

In the first part of the thesis, we formulate the minimization problem of the time required from image capture until the visual features are extracted from the image. The solution to the minimization problem is an allocation of sub-areas of a captured image to a subset of the processing nodes, which perform the feature extraction. We use the temporal correlation of the image contents to predict an approximation of the distribution of visual features in a captured image. Based on the approximate distribution, we compute an approximate solution to the minimization problem using linear programming. We show that the last value predictor gives a good trade-off between performance and computational complexity.

In the second part of the thesis, we propose fully distributed algorithms for allocation of image sub-areas to the processing nodes in a multi-camera Visual Sensor Network. The algorithms differ in the amount of information available and in how allocation updates are applied. We provide analytical results on the existence of equilibrium allocations, and show that an equilibrium allocation may not be optimal. We show that fully distributed algorithms are most efficient when sensors make asynchronous changes to their allocations, and in topologies with less symmetry. However, with the addition of sparse coordination, both average and worst-case performance can be improved significantly.

Abstract [sv]

Allt eftersom digitalkameror blir både billigare och mer avancerade blir de också vanligare i handhållna enheter, i hemelektronik och som dedikerad övervakningsutrustning. Algoritmer för datorseende kan användas på stillbilderna och videoklippen som samlas in av dessa kameror för objektidentifiering, scenförståelse, navigering, mm. Genom att använda data från flera kameror som observerar samma händelser kan prestandan hos dessa datorseendealgoritmer förbättras. Utplacering av kameranätverk är emellertid svårt i områden utan tillgång till infrastruktur som kan tillhandahålla elektricitet och nätverksanslutning. I denna avhandling studerar vi nätverk av kamerautrustade sensornoder som ett kostnadseffektivt alternativ till konventionella kameranätverk. För att övervinna beräkningsbegränsningarna hos sensornoderna förstärker vi sensornätverket med dedikerade beräkningsnoder och bearbetar bilder parallellt i flera beräkningsnoder.

I den första delen av avhandlingen formulerar vi minimeringsproblemet för den tid som krävs från bildupptagning tills en representation av den visuella informationen extraheras från bilden. Lösningen till minimeringsproblemet är en fördelning av delområden av en infångad bild till en delmängd av beräkningsnoderna. Beräkningsnoderna bearbetar bilderna för att ta fram representationen av den visuella informationen. Vi använder den tidsmässiga korrelationen av bildinnehållet för att förutsäga en approximation av fördelningen av visuell information i en infångad bild. Baserat på den ungefärliga fördelningen beräknar vi en approximativ lösning på minimeringsproblemet med hjälp av linjärprogrammering. Vi visar att det går att får en bra kompromiss mellan prestanda och beräkningskomplexitet genom att använda det visuella innehållet i tidigare bildrutor för att förutsäga innehållet i kommande bildrutor.

I den andra delen av avhandlingen föreslår vi helt distribuerade algoritmer för tilldeling av delar av bilder till beräkningsnoder i ett visuellt sensornätverk. Algoritmerna skiljer sig i mängden tillgänglig information och hur uppdateringar av tilldelningar verkställs. Vi tillhandahåller analytiska resultat för förekomsten av jämviktstilldelningar och visar att en given jämviktstilldelning inte nödvändigtvis är optimal. Vi visar även att fullt distribuerade algoritmer är mest effektiva när sensornoder gör asynkrona förändringar i sina tilldelningar och i mindre symmetriska topologier. Genom att lägga till gles koordination kan prestandan förbättras avsevärt både i genomsnitt och i värsta fall.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. 33 p.
Series
TRITA-EE, ISSN 1653-5146 ; 2017:051
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-207094 (URN)978-91-7729-444-3 (ISBN)
Presentation
2017-06-12, Q2, Osquldas väg 10, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20170516

Available from: 2017-05-16 Created: 2017-05-15 Last updated: 2017-05-16Bibliographically approved

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