| Attributes | Values |
|---|
| rdf:type
| |
| Description
| - Článek se zabývá řešením správy metadat pro vizuální dohled. Data přicházející z mnoha kamer jsou anotována pomocí jednotek počítačového vidění, které produkují metadata reprezentující pohybující objekty v jejich stavech. Předpokládáme, že data jsou nespolehlivá, zašuměná a některé stavy chybějí.<br> Řešení spočívá ve třech vrstvách: (a) čištění dat - zvyšuje jejich kvalitu (pomocí Kalmanova filtu), (b) integrace - přiřazuje pohybujícím se objektům globální identifikátor (Bayesovká klasifikace, SVM), (c) persistentní vrstva zajišťuje správu metadat, dotazování v reálném čase, analýzu dat a umožňuje dolování.<br> <br> (cs)
- The paper deals with a solution for visual surveillance metadata management. Data coming from many cameras is annotated using computer vision units to produce metadata representing moving objects in their states. It is assumed that the data is often uncertain, noisy and some states are missing. <p>The solution consists of the following three layers: (a) data cleaning layer - improves quality of the data by smoothing it and by filling in missing states in short sequences referred to as tracks that represent a composite state of a moving object in a spatiotemporal subspace followed by one camera. (b) Data integration layer - assigns a global identity to tracks that represent the same object. (c) Persistence layer - manages the metadata in a database so that it can be used for online identification and offline querying, analyzing and mining. A Kalman filter technique is used to solve (a) and a classification based on the moving object's state and its visual properties is used in (b). An object model for
- The paper deals with a solution for visual surveillance metadata management. Data coming from many cameras is annotated using computer vision units to produce metadata representing moving objects in their states. It is assumed that the data is often uncertain, noisy and some states are missing. <p>The solution consists of the following three layers: (a) data cleaning layer - improves quality of the data by smoothing it and by filling in missing states in short sequences referred to as tracks that represent a composite state of a moving object in a spatiotemporal subspace followed by one camera. (b) Data integration layer - assigns a global identity to tracks that represent the same object. (c) Persistence layer - manages the metadata in a database so that it can be used for online identification and offline querying, analyzing and mining. A Kalman filter technique is used to solve (a) and a classification based on the moving object's state and its visual properties is used in (b). An object model for (en)
|
| Title
| - Visual Surveillance Metadata Management
- Správa metadat pro vizuální dohled (cs)
- Visual Surveillance Metadata Management (en)
|
| skos:prefLabel
| - Visual Surveillance Metadata Management
- Správa metadat pro vizuální dohled (cs)
- Visual Surveillance Metadata Management (en)
|
| skos:notation
| - RIV/00216305:26230/07:PU70857!RIV08-MSM-26230___
|
| http://linked.open.../vavai/riv/strany
| |
| http://linked.open...avai/riv/aktivita
| |
| http://linked.open...avai/riv/aktivity
| |
| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
| |
| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
| |
| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/00216305:26230/07:PU70857
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - Visual surveillance, metadata management, cameras, vision units, moving objects, data cleaning, integration, persistence, Kalman filter, classification, object model. (en)
|
| http://linked.open.../riv/klicoveSlovo
| |
| http://linked.open...ontrolniKodProRIV
| |
| http://linked.open...v/mistoKonaniAkce
| |
| http://linked.open...i/riv/mistoVydani
| |
| http://linked.open...i/riv/nazevZdroje
| - Eighteenth International Workshop on Database and Expert Systems Applications
|
| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...iv/tvurceVysledku
| - Chmelař, Petr
- Zendulka, Jaroslav
|
| http://linked.open...vavai/riv/typAkce
| |
| http://linked.open.../riv/zahajeniAkce
| |
| http://linked.open...n/vavai/riv/zamer
| |
| number of pages
| |
| http://purl.org/ne...btex#hasPublisher
| - IEEE Computer Society Press
|
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
![[RDF Data]](/fct/images/sw-rdf-blue.png)
![[cxml]](/fct/images/cxml_doc.png)
![[csv]](/fct/images/csv_doc.png)
![[text]](/fct/images/ntriples_doc.png)
![[turtle]](/fct/images/n3turtle_doc.png)
![[ld+json]](/fct/images/jsonld_doc.png)
![[rdf+json]](/fct/images/json_doc.png)
![[rdf+xml]](/fct/images/xml_doc.png)
![[atom+xml]](/fct/images/atom_doc.png)
![[html]](/fct/images/html_doc.png)