@inproceedings{4ed35c5c554048b3b54919a5776f11b1,
title = "Fourier domain adaptation for nighttime pedestrian detection using Faster R-CNN",
abstract = "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).",
keywords = "domain adaptation, Fourier transform, infrared images, nighttime surveillance, Object detection",
author = "Lu, {Peggy Joy} and Chuang, {Jen Hui}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 ; Conference date: 27-05-2022 Through 01-06-2022",
year = "2022",
doi = "10.1109/ISCAS48785.2022.9937575",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3155--3159",
booktitle = "IEEE International Symposium on Circuits and Systems, ISCAS 2022",
address = "美國",
}