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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F13%3A86088427%21RIV14-MSM-27240___
rdf:type
n3:Vysledek skos:Concept
rdfs:seeAlso
http://link.springer.com/chapter/10.1007%2F978-3-642-41939-3_22
dcterms:description
In this paper, we present an algorithm for estimating the occupancy of individual parking spaces. Our method is based on a computer analysis of images obtained by a camera system monitoring the activities on a parking lot. The proposed method extensively uses a priori information about the parking lot layout and the general shape of well-parked cars, which is incorporated in a simplified probabilistic car model. Discriminative features are extracted from a normalized image of every parking space, the relevance of these gradient-based features is prioritized via a selective flow, and furthermore, their spatial relationship is revealed through an undirected graphical model. We strive to avoid the training phase to reduce the time required to bring the system into a fully operational state. The reliability of the here devised approach is evaluated on the set of video sequences captured during different phases of a day and the results are compared against the ground truth data. In this paper, we present an algorithm for estimating the occupancy of individual parking spaces. Our method is based on a computer analysis of images obtained by a camera system monitoring the activities on a parking lot. The proposed method extensively uses a priori information about the parking lot layout and the general shape of well-parked cars, which is incorporated in a simplified probabilistic car model. Discriminative features are extracted from a normalized image of every parking space, the relevance of these gradient-based features is prioritized via a selective flow, and furthermore, their spatial relationship is revealed through an undirected graphical model. We strive to avoid the training phase to reduce the time required to bring the system into a fully operational state. The reliability of the here devised approach is evaluated on the set of video sequences captured during different phases of a day and the results are compared against the ground truth data.
dcterms:title
A vision-based algorithm for parking lot utilization evaluation using conditional random fields A vision-based algorithm for parking lot utilization evaluation using conditional random fields
skos:prefLabel
A vision-based algorithm for parking lot utilization evaluation using conditional random fields A vision-based algorithm for parking lot utilization evaluation using conditional random fields
skos:notation
RIV/61989100:27240/13:86088427!RIV14-MSM-27240___
n3:predkladatel
n20:orjk%3A27240
n4:aktivita
n8:S
n4:aktivity
S
n4:dodaniDat
n17:2014
n4:domaciTvurceVysledku
n23:7748078
n4:druhVysledku
n21:D
n4:duvernostUdaju
n16:S
n4:entitaPredkladatele
n19:predkladatel
n4:idSjednocenehoVysledku
59188
n4:idVysledku
RIV/61989100:27240/13:86088427
n4:jazykVysledku
n22:eng
n4:klicovaSlova
occupancy detection; parking lot; vision-based surveillance; image analysis
n4:klicoveSlovo
n5:parking%20lot n5:occupancy%20detection n5:vision-based%20surveillance n5:image%20analysis
n4:kontrolniKodProRIV
[D3F067C4B889]
n4:mistoKonaniAkce
Rethymnon
n4:mistoVydani
London
n4:nazevZdroje
Lecture Notes in Computer Science. Volume 8034
n4:obor
n7:IN
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
1
n4:rokUplatneniVysledku
n17:2013
n4:tvurceVysledku
Fabián, Tomáš
n4:typAkce
n18:WRD
n4:zahajeniAkce
2013-07-29+02:00
s:issn
0302-9743
s:numberOfPages
12
n6:doi
10.1007/978-3-642-41939-3_22
n9:hasPublisher
Springer-Verlag
n15:isbn
978-3-642-41938-6
n14:organizacniJednotka
27240