Fourier domain adaptation for nighttime pedestrian detection using Faster R-CNN

Peggy Joy Lu, Jen Hui Chuang

研究成果: Conference contribution同行評審

摘要

An efficient domain adaptation scheme is presented in this paper for nighttime pedestrian detection using Faster R-CNN. First, we adopt Fourier domain adaptation on training data by replacing low-frequency spectrum of source data (RGB images) with that of target data (infrared images). Such approach is more efficient compared with existing state-of-the-art methods of domain adaptation for object detection, as it does not require adversarial learning, or adding extra components to the Faster R-CNN. In addition, a simple preprocessing of intensity scaling is empirically selected among several image enhancement algorithms for testing data. Experimental results demonstrate that performance improvements of up to 30% and 10% can be achieved with the above processes for training data and testing data, respectively, for an indoor scene with poor illumination condition (while other processes may actually lower the performance).

原文English
主出版物標題IEEE International Symposium on Circuits and Systems, ISCAS 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面3155-3159
頁數5
ISBN(電子)9781665484855
DOIs
出版狀態Published - 2022
事件2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States
持續時間: 27 5月 20221 6月 2022

出版系列

名字Proceedings - IEEE International Symposium on Circuits and Systems
2022-May
ISSN(列印)0271-4310

Conference

Conference2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
國家/地區United States
城市Austin
期間27/05/221/06/22

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