TY - CONF
T1 - Learning to focus and track extreme climate events
AU - Kim, Sookyung
AU - Park, Sunghyun
AU - Chung, Sunghyo
AU - Lee, Joonseok
AU - Lee, Yunsung
AU - Kim, Hyojin
AU - Prabhat, Mr
AU - Choo, Jaegul
N1 - Funding Information:
Acknowledgement. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Lab. under contract DE-AC52-07NA27344(LLNL-CONF-776815). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. NRF-2018M3E3A1057305).
Publisher Copyright:
© 2019. The copyright of this document resides with its authors.
PY - 2020
Y1 - 2020
N2 - This paper tackles the task of extreme climate event tracking. It has unique challenges compared to other visual object tracking problems, including a wider range of spatio-temporal dynamics, the unclear boundary of the target, and the shortage of a labeled dataset. We propose a simple but robust end-to-end model based on multi-layered ConvLSTMs, suitable for climate event tracking. It first learns to imprint the location and the appearance of the target at the first frame in an auto-encoding fashion. Next, the learned feature is fed to the tracking module to track the target in subsequent time frames. To tackle the data shortage problem, we propose data augmentation based on conditional generative adversarial networks. Extensive experiments show that the proposed framework significantly improves tracking performance of a hurricane tracking task over several state-of-the-art methods.
AB - This paper tackles the task of extreme climate event tracking. It has unique challenges compared to other visual object tracking problems, including a wider range of spatio-temporal dynamics, the unclear boundary of the target, and the shortage of a labeled dataset. We propose a simple but robust end-to-end model based on multi-layered ConvLSTMs, suitable for climate event tracking. It first learns to imprint the location and the appearance of the target at the first frame in an auto-encoding fashion. Next, the learned feature is fed to the tracking module to track the target in subsequent time frames. To tackle the data shortage problem, we propose data augmentation based on conditional generative adversarial networks. Extensive experiments show that the proposed framework significantly improves tracking performance of a hurricane tracking task over several state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=85087334020&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85087334020
T2 - 30th British Machine Vision Conference, BMVC 2019
Y2 - 9 September 2019 through 12 September 2019
ER -