The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Deep background subtraction with scene-specific convolutional neural networks
Open accessAbstract
Background subtraction is usually based on low-level or hand-crafted features such as raw color components, gradients, or local binary patterns. As an improvement, we present a background subtraction algorithm based on spatial features learned with convolutional neural networks (ConvNets). Our algorithm uses a background model reduced to a single background image and a scene-specific training dataset to feed ConvNets that prove able to learn how to subtract the background from an input image patch. Experiments led on 2014 ChangeDetection.net dataset show that our ConvNet based algorithm at least reproduces the performance of state-of-the-art methods, and that it even outperforms them significantly when scene-specific knowledge is considered.
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Self-citationA texture-based method for modeling the background and detecting moving objects
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Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
Voigtlaender, P., & Leibe, B. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.116 · Cited by 443
A deep convolutional neural network for video sequence background subtraction
Babaee, M., Dinh, D. T., & Rigoll, G. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.09.040 · Cited by 406
Bouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.024 · Cited by 396
Background subtraction in real applications: Challenges, current models and future directions
Garcia-Garcia, B., Bouwmans, T., & Rosales Silva, A. J. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2019.100204 · Cited by 337
Foreground segmentation using convolutional neural networks for multiscale feature encoding
Lim, L. A., & Yalim Keles, H. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.08.002 · Cited by 305
Metadata
- Authors
- Braham, M., & Van Droogenbroeck, M.
- Venue
- 2016 International Conference on Systems, Signals and Image Processing (IWSSIP)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 1-4
- OpenAlex
- W2417256080
- Cited by
- 313
- References
- 13
- FWCI
- 15.39
- Type
- conference paper
- Language
- en
- Open access
- green
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- Braham, M., & Van Droogenbroeck, M. (2016). Deep background subtraction with scene-specific convolutional neural networks. 2016 International Conference on Systems, Signals and Image Processing (IWSSIP). https://doi.org/10.1109/iwssip.2016.7502717
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
