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Description
  • We propose a learning approach to tracking explicitly minimizing the computational complexity of the tracking process subject to user-defined probability of failure (loss-of-lock) and precision. The tracker is formed by a Number of Sequences of Learned Linear Predictors (NoSLLiP). Robustness of NoSLLiP is achieved by modeling the object as a collection of local motion predictors --- object motion is estimated by the outlier-tolerant Ransac algorithm from local predictions. Efficiency of the NoSLLiP tracker stems from (i) the simplicity of the local predictors and (ii) from the fact that all design decisions - the number of local predictors used by the tracker, their computational complexity (ie the number of observations the prediction is based on), locations as well as the number of Ransac iterations are all subject to the optimization (learning) process. All time-consuming operations are performed during the learning stage - t.
  • We propose a learning approach to tracking explicitly minimizing the computational complexity of the tracking process subject to user-defined probability of failure (loss-of-lock) and precision. The tracker is formed by a Number of Sequences of Learned Linear Predictors (NoSLLiP). Robustness of NoSLLiP is achieved by modeling the object as a collection of local motion predictors --- object motion is estimated by the outlier-tolerant Ransac algorithm from local predictions. Efficiency of the NoSLLiP tracker stems from (i) the simplicity of the local predictors and (ii) from the fact that all design decisions - the number of local predictors used by the tracker, their computational complexity (ie the number of observations the prediction is based on), locations as well as the number of Ransac iterations are all subject to the optimization (learning) process. All time-consuming operations are performed during the learning stage - t. (en)
Title
  • Tracking by an Optimal Sequence of Linear Predictors
  • Tracking by an Optimal Sequence of Linear Predictors (en)
skos:prefLabel
  • Tracking by an Optimal Sequence of Linear Predictors
  • Tracking by an Optimal Sequence of Linear Predictors (en)
skos:notation
  • RIV/68407700:21230/09:00157040!RIV10-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET101210407), P(1M0567), P(GA102/07/1317)
http://linked.open...iv/cisloPeriodika
  • 4
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
  • 346621
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/09:00157040
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Image processing and computer vision; Scene analysis; Tracking (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [4AE5CFA86290]
http://linked.open...i/riv/nazevZdroje
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 31
http://linked.open...iv/tvurceVysledku
  • Matas, Jiří
  • Svoboda, Tomáš
  • Zimmermann, Karel
http://linked.open...ain/vavai/riv/wos
  • 000263396100008
issn
  • 0162-8828
number of pages
http://localhost/t...ganizacniJednotka
  • 21230
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