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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F11%3A00187102%21RIV12-MSM-21230___
rdf:type
n14:Vysledek skos:Concept
dcterms:description
This paper addresses object detection and tracking in high-resolution omnidirectional images. The foreseen application is a visual subsystem of a rescue robot equipped with an omnidirectional camera, which demands real time efficiency and robustness against changing viewpoint. Object detectors typically do not guarantee specific frame rate. The detection time may vastly depend on a scene complexity and image resolution. The adapted tracker can often help to overcome the situation, where the appearance of the object is far from the training set. On the other hand, once a tracker is lost, it almost never finds the object again. We propose a combined solution where a very efficient tracker (based on sequential linear predictors) incrementally accommodates varying appearance and speeds up the whole process. We experimentally show that the performance of the combined algorithm, measured by a ratio between false positives and false negatives, outperforms both individual algorithms. This paper addresses object detection and tracking in high-resolution omnidirectional images. The foreseen application is a visual subsystem of a rescue robot equipped with an omnidirectional camera, which demands real time efficiency and robustness against changing viewpoint. Object detectors typically do not guarantee specific frame rate. The detection time may vastly depend on a scene complexity and image resolution. The adapted tracker can often help to overcome the situation, where the appearance of the object is far from the training set. On the other hand, once a tracker is lost, it almost never finds the object again. We propose a combined solution where a very efficient tracker (based on sequential linear predictors) incrementally accommodates varying appearance and speeds up the whole process. We experimentally show that the performance of the combined algorithm, measured by a ratio between false positives and false negatives, outperforms both individual algorithms.
dcterms:title
Fast Learnable Object Tracking and Detection in High-resolution Omnidirectional Images Fast Learnable Object Tracking and Detection in High-resolution Omnidirectional Images
skos:prefLabel
Fast Learnable Object Tracking and Detection in High-resolution Omnidirectional Images Fast Learnable Object Tracking and Detection in High-resolution Omnidirectional Images
skos:notation
RIV/68407700:21230/11:00187102!RIV12-MSM-21230___
n14:predkladatel
n15:orjk%3A21230
n3:aktivita
n8:P
n3:aktivity
P(7E10044), P(GAP103/10/1585), P(GPP103/11/P700)
n3:dodaniDat
n10:2012
n3:domaciTvurceVysledku
n13:1144359 n13:6464742 n13:9397000
n3:druhVysledku
n16:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
199366
n3:idVysledku
RIV/68407700:21230/11:00187102
n3:jazykVysledku
n11:eng
n3:klicovaSlova
detection; tracking; incremental; learning; predictors; fern; omnidirectional; high-resolution
n3:klicoveSlovo
n6:learning n6:predictors n6:tracking n6:high-resolution n6:omnidirectional n6:incremental n6:detection n6:fern
n3:kontrolniKodProRIV
[34360B28917D]
n3:mistoKonaniAkce
Algarve
n3:mistoVydani
Setúbal
n3:nazevZdroje
Proceedings of VISAPP 2011 International Conference on Computer Vision Theory and Applications
n3:obor
n22:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n4:7E10044 n4:GAP103%2F10%2F1585 n4:GPP103%2F11%2FP700
n3:rokUplatneniVysledku
n10:2011
n3:tvurceVysledku
Zimmermann, Karel Svoboda, Tomáš Hurych, David
n3:typAkce
n19:WRD
n3:zahajeniAkce
2011-03-05+01:00
s:numberOfPages
10
n18:hasPublisher
INSTICC Press
n5:isbn
978-989-8425-47-8
n7:organizacniJednotka
21230