Presentation information

General Session

General Session » [GS] J-3 Data mining

[4P2-J-3] Data mining: spaciotemporal data

Fri. Jun 7, 2019 12:00 PM - 1:40 PM Room P (Front-left room of 1F Exhibition hall)

Chair:Kimihiro Mizutani Reviewer:Masayuki Otani

1:00 PM - 1:20 PM

[4P2-J-3-04] Modeling and tracking spatiotemporal objects by mixture distribution

Application to phased-array weather radar data

Ryo Hayashi1, Yuki Fuji1, 〇Rie HOnda1, Shinsuke Sato2, Takefumi Murata2, Kazuya Muranaga3, Kentaro Ukawa3, Koji Sasa1, Fumie Murata1 (1. Kochi University, 2. NICT, 3. SEC CO.LTD)

Keywords:multivariate normal distribution, mixture model, EM, spatiotemporal object, tracking

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.