The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Foreground segmentation using convolutional neural networks for multiscale feature encoding
Open accessAbstract
Abstract Several methods have been proposed to solve moving objects segmentation problem accurately in different scenes. However, many of them lack the ability of handling various difficult scenarios such as illumination changes, background or camera motion, camouflage effect, shadow etc. To address these issues, we propose two robust encoder-decoder type neural networks that generate multi-scale feature encodings in different ways and can be trained end-to-end using only a few training samples. Using the same encoder-decoder configurations, in the first model, a triplet of encoders take the inputs in three scales to embed an image in a multi-scale feature space; in the second model, a Feature Pooling Module (FPM) is plugged on top of a single input encoder to extract multi-scale features in the middle layers. Both models use a transposed convolutional network in the decoder part to learn a mapping from feature space to image space. In order to evaluate our models, we entered the Change Detection 2014 Challenge (changedetection.net) and our models, namely FgSegNet_M and FgSegNet_S, outperformed all the existing state-of-the-art methods by an average F-Measure of 0.9770 and 0.9804, respectively. We also evaluate our models on SBI2015 and UCSD Background Subtraction datasets. Our source code is made publicly available at https://github.com/lim-anggun/FgSegNet .
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Cited by
Bouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.024 · Cited by 396
Ammar, S., Bouwmans, T., Zaghden, N., & Neji, M. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.0769 · Cited by 43
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Shi, H., Ghahremannezhad, H., & Liu, C. (2022) · 2022 IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist55454.2022.9827759 · Cited by 6
Metadata
- Authors
- Lim, L. A., & Yalim Keles, H.
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 112
- Pages
- 256-262
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2783946051
- Cited by
- 305
- References
- 44
- FWCI
- 19.81
- Type
- journal article
- Language
- en
- Open access
- green
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