The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Le filtre à particules a attiré une attention croissante de la part des chercheurs en suivi d'objets en raison de sa propriété prometteuse de gestion de systèmes non linéaires et non gaussiens. Dans cet article, nous explorons principalement le problème de l’estimation précise des probabilités d’observation de particules dans l’espace spatial commun. À cette fin, un mélange de similitudes basées sur la fonction du noyau gaussien est présenté pour évaluer l'écart entre la région cible et la région des particules. Une telle similarité peut être interprétée comme l’attente de la distribution spatiale pondérée des caractéristiques sur la région cible. Pour adapter l'explosion du mouvement de l'objet, nous présentons également une méthode permettant d'ajuster de manière appropriée le modèle de transition d'état en utilisant les a priori de la vitesse de mouvement et de la taille de l'objet. En comparaison avec le tracker à filtre à particules standard, notre algorithme de suivi affiche les meilleures performances sur les séquences vidéo difficiles.
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Liang SHA, Guijin WANG, Anbang YAO, Xinggang LIN, "Measuring Particles in Joint Feature-Spatial Space" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 7, pp. 1737-1742, July 2009, doi: 10.1587/transfun.E92.A.1737.
Abstract: Particle filter has attracted increasing attention from researchers of object tracking due to its promising property of handling nonlinear and non-Gaussian systems. In this paper, we mainly explore the problem of precisely estimating observation likelihoods of particles in the joint feature-spatial space. For this purpose, a mixture Gaussian kernel function based similarity is presented to evaluate the discrepancy between the target region and the particle region. Such a similarity can be interpreted as the expectation of the spatial weighted feature distribution over the target region. To adapt outburst of object motion, we also present a method to appropriately adjust state transition model by utilizing the priors of motion speed and object size. In comparison with the standard particle filter tracker, our tracking algorithm shows the better performance on challenging video sequences.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1737/_p
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@ARTICLE{e92-a_7_1737,
author={Liang SHA, Guijin WANG, Anbang YAO, Xinggang LIN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Measuring Particles in Joint Feature-Spatial Space},
year={2009},
volume={E92-A},
number={7},
pages={1737-1742},
abstract={Particle filter has attracted increasing attention from researchers of object tracking due to its promising property of handling nonlinear and non-Gaussian systems. In this paper, we mainly explore the problem of precisely estimating observation likelihoods of particles in the joint feature-spatial space. For this purpose, a mixture Gaussian kernel function based similarity is presented to evaluate the discrepancy between the target region and the particle region. Such a similarity can be interpreted as the expectation of the spatial weighted feature distribution over the target region. To adapt outburst of object motion, we also present a method to appropriately adjust state transition model by utilizing the priors of motion speed and object size. In comparison with the standard particle filter tracker, our tracking algorithm shows the better performance on challenging video sequences.},
keywords={},
doi={10.1587/transfun.E92.A.1737},
ISSN={1745-1337},
month={July},}
Copier
TY - JOUR
TI - Measuring Particles in Joint Feature-Spatial Space
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1737
EP - 1742
AU - Liang SHA
AU - Guijin WANG
AU - Anbang YAO
AU - Xinggang LIN
PY - 2009
DO - 10.1587/transfun.E92.A.1737
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2009
AB - Particle filter has attracted increasing attention from researchers of object tracking due to its promising property of handling nonlinear and non-Gaussian systems. In this paper, we mainly explore the problem of precisely estimating observation likelihoods of particles in the joint feature-spatial space. For this purpose, a mixture Gaussian kernel function based similarity is presented to evaluate the discrepancy between the target region and the particle region. Such a similarity can be interpreted as the expectation of the spatial weighted feature distribution over the target region. To adapt outburst of object motion, we also present a method to appropriately adjust state transition model by utilizing the priors of motion speed and object size. In comparison with the standard particle filter tracker, our tracking algorithm shows the better performance on challenging video sequences.
ER -