Learning to Detect Fine-Grained Change Under Variant Imaging Conditions

Rui Huang, Wei Feng, Zezheng Wang, Mingyuan Fan, Liang Wan, Jizhou Sun; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2916-2924

Abstract


Fine-grained change detection under variant imaging conditions is an important and challenging task for high-value scene monitoring in culture heritage. In this paper, we show that after a simple coarse alignment of lighting and camera differences, fine-grained change detection can be reliably solved by a deep network model, which is specifically composed of three functional parts, i.e., camera pose correction network (PCN), fine-grained change detection network (FCDN), and detection confidence boosting. Since our model is properly pre-trained and fine-tuned on both general and specialized data, it exhibits very good generalization capability to produce high-quality minute change detection on real-world scenes under varied imaging conditions. Extensive experiments validate the superior effectiveness and reliability over state-of-the-art methods. We have achieved 67.41% relative F1-measure improvement over the best competitor on real-world benchmark dataset.

Related Material


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[bibtex]
@InProceedings{Huang_2017_ICCV,
author = {Huang, Rui and Feng, Wei and Wang, Zezheng and Fan, Mingyuan and Wan, Liang and Sun, Jizhou},
title = {Learning to Detect Fine-Grained Change Under Variant Imaging Conditions},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}