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A Study on Practical Visual Violence Rating

Research Project

Project/Area Number 17K12714
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Perceptual information processing
Research InstitutionNagoya University

Principal Investigator

WANG YU  名古屋大学, 国際開発研究科, 助教 (60724169)

Project Period (FY) 2017-04-01 – 2019-03-31
Project Status Completed (Fiscal Year 2018)
Budget Amount *help
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Keywords暴力度レーティング / 視覚的暴力 / 行動認識 / 画像 / 機械学習
Outline of Final Research Achievements

In this work, in order to develop high performance practical visual violence rating approach, we worked on the following four aspects: (1) human detection-based spatial-temporal feature pooling methods; (2) graphical model-based methods for optimizing violent attribute estimations; (3) list-wise ranking based violence rating methods; and (4) spatial-temporal parallel processing methods. In order to evaluate these methods, we collected and labeled a large number of video clips, and conducted experiments quantitatively. We confirmed that these methods lead to significant improvements on rating speed and rating accuracy over the previous works, and push the visual violence rating technique one step forward toward the practical usage.

Academic Significance and Societal Importance of the Research Achievements

本研究で開発した暴力度レーティング技術は実用性が高く,実用化出来れば,従来ほとんど手動で行われてきた膨大な数のビデオコンテンツの分類を自動化できる.また,この技術により推定した暴力度から,ビデオごとに視聴に適した年齢層を見積もることができるため,青少年をはじめとする多くの利用者にとって,より自由で安全な視聴空間が確保できる.
本研究で暴力度レーティングの実用化のために開発した要素技術は,暴力度レーティングの精度と速度を大幅に向上させることが出来た他,映像認識タスクにおいて最も基本的な課題の解決にも貢献できる.研究開発成果はビデオ分類,要約,検索等の研究にも大きな推進力を与えることになる.

Report

(3 results)
  • 2018 Annual Research Report   Final Research Report ( PDF )
  • 2017 Research-status Report
  • Research Products

    (13 results)

All 2019 2018 2017

All Journal Article (1 results) (of which Peer Reviewed: 1 results) Presentation (12 results) (of which Int'l Joint Research: 7 results)

  • [Journal Article] A Novel Approach for Annotation-based Image Retrieval Using Deep Architecture2019

    • Author(s)
      Guanwen Zhang, Jien Kato, Yu Wang, Kenji Mase
    • Journal Title

      Journal of Multiple-Valued Logic and Soft Computing

      Volume: 30 Pages: 541-558

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed
  • [Presentation] Good Choices for Deep Convolutional Feature Encoding2019

    • Author(s)
      Yu Wang and Jien Kato
    • Organizer
      IEEE Winter Conference on Applications of Computer Vision 2019
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Good Practice of Using Deep Features in Content-based Image Retrieval2019

    • Author(s)
      Longjiao Zhao, Yu Wang, Jien Kato and Hiromi Tanaka
    • Organizer
      25th International Workshop on Frontiers of Computer Vision
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Good Deep Features for Pedestrian Detection2019

    • Author(s)
      Misaki Kodaira, Yu Wang, Jien Kato, Hiroshi Murase and Hiromi Tanaka
    • Organizer
      25th International Workshop on Frontiers of Computer Vision
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Supervised Spatial Transformer Networks for Attention Learning in Fine-grained Action Recognition2019

    • Author(s)
      Dichao Liu, Yu Wang and Jien Kato
    • Organizer
      International Conference on Computer Vision Theory and Applications 2019
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] A Fine-grained Dataset for Visual Violence Analysis2018

    • Author(s)
      Ying Ji, Yu Wang and Jien Kato
    • Organizer
      画像の認識・理解シンポジウム2018
    • Related Report
      2018 Annual Research Report
  • [Presentation] 深層ローカル特徴を用いた歩行者検出2018

    • Author(s)
      小平美沙季, 王彧, 加藤ジェーン
    • Organizer
      画像の認識・理解シンポジウム2018
    • Related Report
      2018 Annual Research Report
  • [Presentation] Image Retrieval with Augmented Fine-tuned CNN Features2018

    • Author(s)
      Longjiao Zhao, Yu Wang and Jien Kato
    • Organizer
      パターン認識・メディア理解研究会(青山学院大学,2018.3.18)
    • Related Report
      2017 Research-status Report
  • [Presentation] Hierarchical Multi-stream STNs for Fine-grained Action Recognition2017

    • Author(s)
      Dichao Liu, Yu Wang and Jien Kato
    • Organizer
      パターン認識・メディア理解研究会(慶応大学,2017.12.16)
    • Related Report
      2017 Research-status Report
  • [Presentation] Collision Risk Rating of Traffic Scene from Dashboard Cameras2017

    • Author(s)
      Yu Wang and Jien Kato
    • Organizer
      2017 International Conference on Digital Image Computing: Techniques and Application(Sydney, Nov.29-Dec.1, 2017)
    • Related Report
      2017 Research-status Report
    • Int'l Joint Research
  • [Presentation] Evaluation of Triple-Stream Convolutional Networks for Action Recognition2017

    • Author(s)
      Dichao Liu, Yu Wang and Jien Kato
    • Organizer
      2017 International Conference on Digital Image Computing: Techniques and Application(Sydney, Nov.29-Dec.1, 2017)
    • Related Report
      2017 Research-status Report
    • Int'l Joint Research
  • [Presentation] Local Features Augmenting for Better Image Retrieval2017

    • Author(s)
      Longjiao Zhao, Yu Wang and Jien Kato
    • Organizer
      2017 International Conference on Digital Image Computing: Techniques and Application(Sydney, Nov.29-Dec.1, 2017)
    • Related Report
      2017 Research-status Report
    • Int'l Joint Research
  • [Presentation] Spatial Attention Selection for Fine-grained Action Recognition2017

    • Author(s)
      Dichao Liu, Yu Wang and Jien Kato
    • Organizer
      電気関係学会東海支部連合大会(名古屋大学,2017.9.8)
    • Related Report
      2017 Research-status Report

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Published: 2017-04-28   Modified: 2020-03-30  

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