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
Penapis zarah telah menarik perhatian yang semakin meningkat daripada penyelidik penjejakan objek kerana sifatnya yang menjanjikan untuk mengendalikan sistem bukan linear dan bukan Gaussian. Dalam makalah ini, kami terutamanya meneroka masalah menganggarkan dengan tepat kemungkinan pemerhatian zarah dalam ruang ciri-ruang bersama. Untuk tujuan ini, campuran berasaskan fungsi isirong Gaussian dipersembahkan untuk menilai percanggahan antara kawasan sasaran dan kawasan zarah. Persamaan sedemikian boleh ditafsirkan sebagai jangkaan taburan ciri berwajaran spatial ke atas kawasan sasaran. Untuk menyesuaikan ledakan gerakan objek, kami juga membentangkan kaedah untuk melaraskan model peralihan keadaan dengan sewajarnya dengan menggunakan keutamaan kelajuan gerakan dan saiz objek. Berbanding dengan penjejak penapis zarah standard, algoritma penjejakan kami menunjukkan prestasi yang lebih baik pada jujukan video yang mencabar.
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Salinan
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
Salinan
@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},}
Salinan
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 -