Particle filters are advanced for building robust object trackers capable of operation under difficult tracking conditions. An Excitation Particle Filter (EPF) is introduced in this book for object tracking. A new likelihood model is proposed. It depends on multiple likelihood functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modify the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. The proposed enhanced PF (EPF) is implemented in software and evaluated. Simulation results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness and occlusion over classical tracking algorithms. Three efficient novel hardware architectures of the Sample Important Resample Filter (SIRF) and the EPF are introduced and implemented on FPGA platform. These architectures feature speed improvement, efficient memory utilization, and/or hardware resource saving.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Lecturer at Department of Computers and Systems Engineering , Faculty of Engineering, Zagazig University. Lecturer at Nuclear Research Center. PhD. and Msc. in Electronic and Communication from Faculty of Engineering, Cairo University 2010 and 2002. Bsc. in Electronic and Communication from Faculty of Engineering, Zagazig University 1993.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Particle filters are advanced for building robust object trackers capable of operation under difficult tracking conditions. An Excitation Particle Filter (EPF) is introduced in this book for object tracking. A new likelihood model is proposed. It depends on multiple likelihood functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modify the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. The proposed enhanced PF (EPF) is implemented in software and evaluated. Simulation results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness and occlusion over classical tracking algorithms. Three efficient novel hardware architectures of the Sample Important Resample Filter (SIRF) and the EPF are introduced and implemented on FPGA platform. These architectures feature speed improvement, efficient memory utilization, and/or hardware resource saving. 120 pp. Englisch. N° de réf. du vendeur 9783659243486
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abd El-Halym HowidaLecturer at Department of Computers and Systems Engineering , Faculty of Engineering, Zagazig University. Lecturer at Nuclear Research Center. PhD. and Msc. in Electronic and Communication from Faculty of Engineeri. N° de réf. du vendeur 159139151
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Particle filters are advanced for building robust object trackers capable of operation under difficult tracking conditions. An Excitation Particle Filter (EPF) is introduced in this book for object tracking. A new likelihood model is proposed. It depends on multiple likelihood functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modify the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. The proposed enhanced PF (EPF) is implemented in software and evaluated. Simulation results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness and occlusion over classical tracking algorithms. Three efficient novel hardware architectures of the Sample Important Resample Filter (SIRF) and the EPF are introduced and implemented on FPGA platform. These architectures feature speed improvement, efficient memory utilization, and/or hardware resource saving.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 120 pp. Englisch. N° de réf. du vendeur 9783659243486
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Particle filters are advanced for building robust object trackers capable of operation under difficult tracking conditions. An Excitation Particle Filter (EPF) is introduced in this book for object tracking. A new likelihood model is proposed. It depends on multiple likelihood functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modify the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. The proposed enhanced PF (EPF) is implemented in software and evaluated. Simulation results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness and occlusion over classical tracking algorithms. Three efficient novel hardware architectures of the Sample Important Resample Filter (SIRF) and the EPF are introduced and implemented on FPGA platform. These architectures feature speed improvement, efficient memory utilization, and/or hardware resource saving. N° de réf. du vendeur 9783659243486
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Particle Filters for Object Tracking | Enhanced Algorithm and Efficient Implementations | Howida Abd El-Halym (u. a.) | Taschenbuch | 120 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9783659243486 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 113177471
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
Paperback. Etat : Brand New. 120 pages. 8.66x5.91x0.28 inches. In Stock. N° de réf. du vendeur __3659243485
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
Paperback. Etat : Brand New. 120 pages. 8.66x5.91x0.28 inches. In Stock. N° de réf. du vendeur 3659243485
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