Individuality-Preserving Silhouette Extraction for Gait Recognition and Its Speedup

• IWAMURA Masakazu

  Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University

• MORI Shunsuke

  Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University

• NAKAMURA Koichiro

  Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University

• TANOUE Takuya

  Osaka University

• UTSUMI Yuzuko

  Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University

• MAKIHARA Yasushi

  Osaka University

• MURAMATSU Daigo

  Osaka University

• KISE Koichi

  Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University

• YAGI Yasushi

  Osaka University

抄録

Most gait recognition approaches rely on silhouette-based representations due to high recognition accuracy and computational efficiency. A fundamental problem for those approaches is how to extract individuality-preserved silhouettes from real scenes accurately. Foreground colors may be similar to background colors, and the background is cluttered. Therefore, we propose a method of individuality-preserving silhouette extraction for gait recognition using standard gait models (SGMs) composed of clean silhouette sequences of various training subjects as shape priors. The SGMs are smoothly introduced into a well-established graph-cut segmentation framework. Experiments showed that the proposed method achieved better silhouette extraction accuracy by more than 2.3% than representative methods and better identification rate of gait recognition (improved by more than 11.0% at rank 20). Besides, to reduce the computation cost, we introduced approximation in the calculation of dynamic programming. As a result, without reducing the segmentation accuracy, we reduced 85.0% of the computational cost.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E104.D (7), 992-1001, 2021-07-01

  一般社団法人 電子情報通信学会

キーワード

• silhouette extraction
• gait recognition
• shape prior
• graph-cut segmentation
• nearest neighbor search

詳細情報

• CRID: 1390570022235778304
• NII論文ID: 130008060180
• DOI: 10.1587/transinf.2020zdp7500
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E104.D/7/E104.D_2020ZDP7500/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可