Modeling and tracking spatiotemporal objects by mixture distribution

• HAYASHI Ryo

  Kochi University

• FUJI Yuki

  Kochi University

• HONDA Rie

  Kochi University

• SATO Shinsuke

  NICT

• MURATA Takefumi

  NICT

• MURANAGA Kazuya

  SEC CO.LTD

• UKAWA Kentaro

  SEC CO.LTD

• SASA Koji

  Kochi University

• MURATA Fumie

  Kochi University

Bibliographic Information

Other Title

• 時空間オブジェクトの混合分布によるモデリングと追跡
• Application to phased-array weather radar data
• フェーズドアレイ気象レーダデータへの適用

Abstract

We propose a new method to detect and track spatiotemporal objects via mixture mode of multivariate normal distribution. Modified Greedy EM algorithm is used to obtain emerged and vanished components. As a post-processing, event-type such as fusion or separation are distinguished by comparing current parameters and previous parameters and each component is labelled to reflect the event. The method is applied to the analysis of phased array weather radar data analysis.

Journal

• Proceedings of the Annual Conference of JSAI

  Proceedings of the Annual Conference of JSAI JSAI2019 (0), 4P2J304-4P2J304, 2019

  The Japanese Society for Artificial Intelligence

Keywords

• multivariate normal distribution
• mixture model
• EM
• spatiotemporal object
• tracking

Details

• CRID: 1390282763120139648
• NII Article ID: 130007658975
• DOI: 10.11517/pjsai.jsai2019.0_4p2j304
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed