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
Dalam penyelidikan ini, kami memberi tumpuan kepada cara menjejak kawasan sasaran yang terletak di sebelah kawasan yang serupa (cth. lengan bawah dan lengan atas) dalam imej zum masuk. Banyak kaedah penjejakan sebelumnya menyatakan kawasan sasaran (iaitu bahagian dalam badan manusia) dengan model tunggal seperti elips, segi empat tepat dan kawasan tertutup boleh ubah bentuk. Dengan model tunggal, walau bagaimanapun, adalah sukar untuk menjejaki kawasan sasaran dalam imej zum masuk tanpa mengelirukan kawasan itu dan kawasan jirannya yang serupa (cth. "lengan bawah dan lengan atas" dan "kawasan kecil dalam batang tubuh dan kawasan jirannya ") kerana mereka mungkin mempunyai corak tekstur yang sama dan tidak mempunyai sempadan yang boleh dikesan di antara mereka. Dalam kaedah kami, sekumpulan titik ciri dalam kawasan sasaran diekstrak dan dijejaki sebagai model sasaran. Perbezaan kecil antara kawasan jiran boleh disahkan dengan memfokuskan hanya pada titik ciri. Di samping itu, (1) kestabilan penjejakan dipertingkatkan menggunakan penapisan zarah dan (2) pengesanan teguh kepada oklusi direalisasikan dengan mengeluarkan titik tidak boleh dipercayai menggunakan pensampelan rawak. Keputusan eksperimen menunjukkan keberkesanan kaedah kami walaupun semasa oklusi berlaku.
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Salinan
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
Salinan
@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},}
Salinan
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 -