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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cette recherche, nous nous concentrons sur la manière de suivre une région cible située à côté de régions similaires (par exemple un avant-bras et le haut du bras) dans des images zoomées. De nombreuses méthodes de suivi antérieures expriment la région cible (c'est-à-dire une partie d'un corps humain) avec un modèle unique tel qu'une ellipse, un rectangle et une région fermée déformable. Cependant, avec le modèle unique, il est difficile de suivre la région cible dans des images zoomées sans la confondre avec ses régions similaires voisines (par exemple « un avant-bras et le haut du bras » et « une petite région dans un torse et ses régions voisines). "), car ils peuvent avoir les mêmes motifs de texture et ne pas avoir de frontière détectable entre eux. Dans notre méthode, un groupe de points caractéristiques dans une région cible est extrait et suivi en tant que modèle de la cible. De petites différences entre les régions voisines peuvent être vérifiées en se concentrant uniquement sur les points caractéristiques. De plus, (1) la stabilité du suivi est améliorée grâce au filtrage des particules et (2) un suivi robuste aux occlusions est réalisé en supprimant les points peu fiables à l'aide d'un échantillonnage aléatoire. Les résultats expérimentaux démontrent l'efficacité de notre méthode même lorsque des occlusions se produisent.
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Norimichi UKITA, Akira MAKINO, Masatsugu KIDODE, "Real-Time Uncharacteristic-Part Tracking with a Point Set" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 7, pp. 1682-1689, July 2010, doi: 10.1587/transinf.E93.D.1682.
Abstract: In this research, we focus on how to track a target region that lies next to similar regions (e.g. a forearm and an upper arm) in zoom-in images. Many previous tracking methods express the target region (i.e. a part in a human body) with a single model such as an ellipse, a rectangle, and a deformable closed region. With the single model, however, it is difficult to track the target region in zoom-in images without confusing it and its neighboring similar regions (e.g. "a forearm and an upper arm" and "a small region in a torso and its neighboring regions") because they might have the same texture patterns and do not have the detectable border between them. In our method, a group of feature points in a target region is extracted and tracked as the model of the target. Small differences between the neighboring regions can be verified by focusing only on the feature points. In addition, (1) the stability of tracking is improved using particle filtering and (2) tracking robust to occlusions is realized by removing unreliable points using random sampling. Experimental results demonstrate the effectiveness of our method even when occlusions occur.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1682/_p
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@ARTICLE{e93-d_7_1682,
author={Norimichi UKITA, Akira MAKINO, Masatsugu KIDODE, },
journal={IEICE TRANSACTIONS on Information},
title={Real-Time Uncharacteristic-Part Tracking with a Point Set},
year={2010},
volume={E93-D},
number={7},
pages={1682-1689},
abstract={In this research, we focus on how to track a target region that lies next to similar regions (e.g. a forearm and an upper arm) in zoom-in images. Many previous tracking methods express the target region (i.e. a part in a human body) with a single model such as an ellipse, a rectangle, and a deformable closed region. With the single model, however, it is difficult to track the target region in zoom-in images without confusing it and its neighboring similar regions (e.g. "a forearm and an upper arm" and "a small region in a torso and its neighboring regions") because they might have the same texture patterns and do not have the detectable border between them. In our method, a group of feature points in a target region is extracted and tracked as the model of the target. Small differences between the neighboring regions can be verified by focusing only on the feature points. In addition, (1) the stability of tracking is improved using particle filtering and (2) tracking robust to occlusions is realized by removing unreliable points using random sampling. Experimental results demonstrate the effectiveness of our method even when occlusions occur.},
keywords={},
doi={10.1587/transinf.E93.D.1682},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Real-Time Uncharacteristic-Part Tracking with a Point Set
T2 - IEICE TRANSACTIONS on Information
SP - 1682
EP - 1689
AU - Norimichi UKITA
AU - Akira MAKINO
AU - Masatsugu KIDODE
PY - 2010
DO - 10.1587/transinf.E93.D.1682
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2010
AB - In this research, we focus on how to track a target region that lies next to similar regions (e.g. a forearm and an upper arm) in zoom-in images. Many previous tracking methods express the target region (i.e. a part in a human body) with a single model such as an ellipse, a rectangle, and a deformable closed region. With the single model, however, it is difficult to track the target region in zoom-in images without confusing it and its neighboring similar regions (e.g. "a forearm and an upper arm" and "a small region in a torso and its neighboring regions") because they might have the same texture patterns and do not have the detectable border between them. In our method, a group of feature points in a target region is extracted and tracked as the model of the target. Small differences between the neighboring regions can be verified by focusing only on the feature points. In addition, (1) the stability of tracking is improved using particle filtering and (2) tracking robust to occlusions is realized by removing unreliable points using random sampling. Experimental results demonstrate the effectiveness of our method even when occlusions occur.
ER -