An algorithm for tracking guitarists' fingertips based on CNN-segmentation and ROI associated particle filter

Zhao Wang, Jun Ohya*

*この研究の対応する著者

研究成果: Article査読

抄録

In this paper, we propose a new approach to tracking the fingertips of guitarists by embedding a CNN-based segmentation module and a temporal grouping-based ROI-association module combined with a particle filter. First, a CNN architecture is trained to segment hand area of each frame of input video. Then, four fingertip candidates (fore, middle, ring and little fingertips) on each frame are located by counting the vote number of template matching (TM) and reversed Hough transform (RHT). Furthermore, temporal grouping-based ROI association is applied to removal noise and group the fingertip candidates on consecutive frames. Finally, particles are distributed between associated fingertip candidates on every two adjacent frames for tracking the fingertips of guitarists. Experiments using videos containing multiple persons' guitar plays under different conditions demonstrate that the proposed method outperforms the current state-of-the-art tracking algorithm in terms of the hand area segmentation accuracy (98%) and the fingertip tracking mean error (5.16 pixel: 0.22 cm on the guitar neck) as well as computation efficiency.

本文言語English
論文番号020506-1
ジャーナルJournal of Imaging Science and Technology
63
2
DOI
出版ステータスPublished - 2019 1月 1

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 化学 (全般)
  • 原子分子物理学および光学
  • コンピュータ サイエンスの応用

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