Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/146964
Title: Joint Tracking and Ground Plane Estimation
Authors: Junseok Kwon;Ralf Dragon;Luc Van Gool
subject: Ground plane estimation|object tracking|particle Markov chain Monte Carlo (Particle MCMC)
Year: 2016
Publisher: IEEE
Abstract: We propose a novel framework that jointly estimates the ground plane and a target's motion trajectory. This results in improvements for both. Estimating their joint posterior is based on Particle Markov Chain Monte Carlo (Particle MCMC). In Particle MCMC, the best target state is inferred by a particle filter and the best ground plane is obtained by MCMC. Compared with conventional sampling methods that iteratively infer the best target states and ground plane parameters, our method infers them jointly. This reduces sampling errors drastically. Experimental results demonstrate that our method outperforms several state-of-the-art tracking methods, while the ground plane accuracy is also improved.
URI: http://localhost/handle/Hannan/146964
ISSN: 1070-9908
1558-2361
volume: 23
issue: 11
More Information: 1514
1517
Appears in Collections:2016

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Title: Joint Tracking and Ground Plane Estimation
Authors: Junseok Kwon;Ralf Dragon;Luc Van Gool
subject: Ground plane estimation|object tracking|particle Markov chain Monte Carlo (Particle MCMC)
Year: 2016
Publisher: IEEE
Abstract: We propose a novel framework that jointly estimates the ground plane and a target's motion trajectory. This results in improvements for both. Estimating their joint posterior is based on Particle Markov Chain Monte Carlo (Particle MCMC). In Particle MCMC, the best target state is inferred by a particle filter and the best ground plane is obtained by MCMC. Compared with conventional sampling methods that iteratively infer the best target states and ground plane parameters, our method infers them jointly. This reduces sampling errors drastically. Experimental results demonstrate that our method outperforms several state-of-the-art tracking methods, while the ground plane accuracy is also improved.
URI: http://localhost/handle/Hannan/146964
ISSN: 1070-9908
1558-2361
volume: 23
issue: 11
More Information: 1514
1517
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7546929.pdf873.55 kBAdobe PDFThumbnail
Preview File
Title: Joint Tracking and Ground Plane Estimation
Authors: Junseok Kwon;Ralf Dragon;Luc Van Gool
subject: Ground plane estimation|object tracking|particle Markov chain Monte Carlo (Particle MCMC)
Year: 2016
Publisher: IEEE
Abstract: We propose a novel framework that jointly estimates the ground plane and a target's motion trajectory. This results in improvements for both. Estimating their joint posterior is based on Particle Markov Chain Monte Carlo (Particle MCMC). In Particle MCMC, the best target state is inferred by a particle filter and the best ground plane is obtained by MCMC. Compared with conventional sampling methods that iteratively infer the best target states and ground plane parameters, our method infers them jointly. This reduces sampling errors drastically. Experimental results demonstrate that our method outperforms several state-of-the-art tracking methods, while the ground plane accuracy is also improved.
URI: http://localhost/handle/Hannan/146964
ISSN: 1070-9908
1558-2361
volume: 23
issue: 11
More Information: 1514
1517
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7546929.pdf873.55 kBAdobe PDFThumbnail
Preview File