抄録
Towards the actualization of an automatic guitar teaching system that can supervise guitar players, this paper proposes an algorithm for accurately and robustly tracking the 3D position of the fretboard from the video of guitar plays. First, we detect the SIFT features within the guitar fretboard and then match the detected points using KD-tree searching based matching algorithm frame by frame to track the whole fretboard. However, during the guitar plays, due to movements of the guitar neck or occlusions caused by guitar players' fingers, the feature points on the fretboard cannot always be matched accurately even though applying traditional RANSAC homography. Therefore, by using our modified RANSAC algorithm to filter out the matching error of the feature points, perspective transformation matrix is obtained between the correctly matched feature points detected at the first and other frames. Consequently, the guitar neck is tracked correctly based on the perspective transformation matrix. Experiments show promising results such as high accuracy: the total mean tracking error of only 4.17 mm and variance of 1.5 for the four tracked corners of the fretboard. This indicates the proposed method outperforms related tracking works including state-of-art Fully-convolutional Network.
| 元の言語 | English |
|---|---|
| 記事番号 | 460 |
| ジャーナル | IS and T International Symposium on Electronic Imaging Science and Technology |
| 巻 | Part F138651 |
| DOI | |
| 出版物ステータス | Published - 2018 1 1 |
| イベント | 3D Image Processing, Measurement (3DIPM), and Applications 2018 - Burlingame, United States 継続期間: 2018 1 28 → 2018 2 1 |
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ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Science Applications
- Human-Computer Interaction
- Software
- Electrical and Electronic Engineering
- Atomic and Molecular Physics, and Optics
これを引用
An accurate and robust algorithm for tracking guitar neck in 3D based on modified RANSAC homography. / Wang, Zhao; Ohya, Jun.
：: IS and T International Symposium on Electronic Imaging Science and Technology, 巻 Part F138651, 460, 01.01.2018.研究成果: Conference article
}
TY - JOUR
T1 - An accurate and robust algorithm for tracking guitar neck in 3D based on modified RANSAC homography
AU - Wang, Zhao
AU - Ohya, Jun
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Towards the actualization of an automatic guitar teaching system that can supervise guitar players, this paper proposes an algorithm for accurately and robustly tracking the 3D position of the fretboard from the video of guitar plays. First, we detect the SIFT features within the guitar fretboard and then match the detected points using KD-tree searching based matching algorithm frame by frame to track the whole fretboard. However, during the guitar plays, due to movements of the guitar neck or occlusions caused by guitar players' fingers, the feature points on the fretboard cannot always be matched accurately even though applying traditional RANSAC homography. Therefore, by using our modified RANSAC algorithm to filter out the matching error of the feature points, perspective transformation matrix is obtained between the correctly matched feature points detected at the first and other frames. Consequently, the guitar neck is tracked correctly based on the perspective transformation matrix. Experiments show promising results such as high accuracy: the total mean tracking error of only 4.17 mm and variance of 1.5 for the four tracked corners of the fretboard. This indicates the proposed method outperforms related tracking works including state-of-art Fully-convolutional Network.
AB - Towards the actualization of an automatic guitar teaching system that can supervise guitar players, this paper proposes an algorithm for accurately and robustly tracking the 3D position of the fretboard from the video of guitar plays. First, we detect the SIFT features within the guitar fretboard and then match the detected points using KD-tree searching based matching algorithm frame by frame to track the whole fretboard. However, during the guitar plays, due to movements of the guitar neck or occlusions caused by guitar players' fingers, the feature points on the fretboard cannot always be matched accurately even though applying traditional RANSAC homography. Therefore, by using our modified RANSAC algorithm to filter out the matching error of the feature points, perspective transformation matrix is obtained between the correctly matched feature points detected at the first and other frames. Consequently, the guitar neck is tracked correctly based on the perspective transformation matrix. Experiments show promising results such as high accuracy: the total mean tracking error of only 4.17 mm and variance of 1.5 for the four tracked corners of the fretboard. This indicates the proposed method outperforms related tracking works including state-of-art Fully-convolutional Network.
UR - http://www.scopus.com/inward/record.url?scp=85052872734&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052872734&partnerID=8YFLogxK
U2 - 10.2352/ISSN.2470-1173.2018.18.3DIPM-460
DO - 10.2352/ISSN.2470-1173.2018.18.3DIPM-460
M3 - Conference article
AN - SCOPUS:85052872734
VL - Part F138651
JO - IS and T International Symposium on Electronic Imaging Science and Technology
JF - IS and T International Symposium on Electronic Imaging Science and Technology
SN - 2470-1173
M1 - 460
ER -