2020 Fiscal Year Final Research Report
Construction of data-driven simulations through video analysis of self-propelled particle systems
| Project/Area Number |
18K11338
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 60100:Computational science-related
|
|---|
| Research Institution | Oita University |
|---|
Principal Investigator |
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
小林 泰三 九州大学, 情報基盤研究開発センター, 学術研究員 (20467880)
下川 倫子 福岡工業大学, 工学部, 助教 (80554419)
|
|---|
| Project Period (FY) |
2018-04-01 – 2021-03-31
|
| Keywords | 自己駆動粒子 / 時系列解析 / 映像分析 / 動的モード分解 |
|---|
| Outline of Final Research Achievements |
Research was conducted for the purpose of modeling complex particle systems and constructing simulation. First, we analyzed the time series of the self-driven particle system and showed that the change point of complex motion can be detected by a method applying dynamic mode decomposition. Furthermore, time-series data was extracted based on the experimental images, a mathematical model for expressing this motion was estimated, and statistical analysis including causal analysis was performed. As a result, we successfully established the procedure from video to the mathematical model even for unknown motion.
|
| Free Research Field |
計算科学
|
| Academic Significance and Societal Importance of the Research Achievements |
画像処理技術は大きく進んでおり、特に、物体の自動認識技術は近年格段に進歩してきた。本研究では、この画像処理技術を複雑な運動の分析に応用し、時系列解析の技術と合わせて数理モデル化を実施した。本研究で対象とした自己駆動粒子系は、動物や人の動きを表現するモデルとして利用される場合もあり、監視カメラや車載カメラで記録された映像の自動分析などへと発展的に応用が可能な研究成果である。
|
