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Data Augmentation for Wildlife Animal Recognition Using Style Transfer

Authors
Jung, S.Lee, Y.Lee, I.Kang, J.Lim, S.Choi, Jongwon
Issue Date
Oct-2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Data Augmentation; Style Transfer; Wildlife Classification
Citation
2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022
Journal Title
2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/61180
DOI
10.1109/ICCE-Asia57006.2022.9954795
ISSN
0000-0000
Abstract
Many military facilities are located in the mountain and the forests, so the wildlife animals easily pass through the facilities. To prevent their invasion, military facilities developed an advanced surveillance system to detect wildlife animals, but the insufficient data for nighttime animals has been a challenging problem. To solve the issue, we design two methods to augment and utilize the training data for nighttime wildlife animals by using a style transfer. The first method is designed to transfer the daytime data to be a style of nighttime data, and the second method exchanges the style of nighttime data with that of daytime data. Through the experiments, we show the effectiveness of the two methods, analyzing the augmented training data. © 2022 IEEE.
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첨단영상대학원 (영상학과)
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