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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00003112naa a2200469 4500
001oai:DiVA.org:kth-157981
003SwePub
008141218s2014 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1579812 URI
0247 a https://doi.org/10.1145/2659021.26694772 DOI
040 a (SwePub)kth
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a kon2 swepub-publicationtype
1001 a Baroffio, L.4 aut
2451 0a Demo :b Enabling image analysis tasks in visual sensor networks
264 c 2014-11-04
264 1a New York, NY, USA :b Association for Computing Machinery (ACM),c 2014
338 a print2 rdacarrier
500 a QC 20141218
520 a This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance.
650 7a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Kommunikationssystem0 (SwePub)202032 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Communication Systems0 (SwePub)202032 hsv//eng
653 a ARM
653 a Binary local visual features
653 a Object recognition
653 a Object tracking
653 a Visual sensor networks
7001 a Canclini, A.4 aut
7001 a Cesana, M.4 aut
7001 a Redondi, A.4 aut
7001 a Tagliasacchi, M.4 aut
7001 a Dán, Györgyu KTH,Kommunikationsnät4 aut0 (Swepub:kth)u1arm91c
7001 a Eriksson, Emilu KTH,Kommunikationsnät4 aut0 (Swepub:kth)u1du7wot
7001 a Fodor, Viktoriau KTH,Kommunikationsnät4 aut0 (Swepub:kth)u1t0vv0n
7001 a Ascenso, J.4 aut
7001 a Monteiro, P.4 aut
7102 a KTHb Kommunikationsnät4 org
7730 t Proceedings of the 8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014d New York, NY, USA : Association for Computing Machinery (ACM)g , s. a46-q <a46-z 9781450329255
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157981
8564 8u https://doi.org/10.1145/2659021.2669477

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