The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
A deep convolutional neural network for video sequence background subtraction
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 14,214
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784637 · Cited by 6,923
Non-parametric Model for Background Subtraction
Elgammal, A., Harwood, D., & Davis, L. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45053-x_48 · Cited by 2,118
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
Barnich, O., & Van Droogenbroeck, M. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2101613 · Cited by 1,885
Wallflower: principles and practice of background maintenance
Toyama, K., Krumm, J., Brumitt, B., & Meyers, B. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.791228 · Cited by 1,696
A Bayesian computer vision system for modeling human interactions
Oliver, N. M., Rosario, B., & Pentland, A. P. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868684 · Cited by 1,578
Real-time foreground–background segmentation using codebook model
Kim, K., Chalidabhongse, T. H., Harwood, D., & Davis, L. (2005) · Real-Time Imaging · doi:10.1016/j.rti.2004.12.004 · Cited by 1,422
A texture-based method for modeling the background and detecting moving objects
Heikkila, M., & Pietikainen, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.68 · Cited by 1,249
CDnet 2014: An Expanded Change Detection Benchmark Dataset
Wang, Y., Jodoin, P.-M., Porikli, F., Konrad, J., Benezeth, Y., & Ishwar, P. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.126 · Cited by 882
Cited by
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,620
Convolutional neural network: a review of models, methodologies and applications to object detection
Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119
Sensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730
Recognition in Terra Incognita
Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679
Intelligent video surveillance: A review through deep learning techniques for crowd analysis
Sreenu, G., & Saleem Durai, M. A. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0212-5 · Cited by 460
Metadata
- Authors
- Babaee, M., Dinh, D. T., & Rigoll, G.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 76
- Pages
- 635-649
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2759692151
- Cited by
- 406
- References
- 23
- FWCI
- 20.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- Babaee, M., Dinh, D. T., & Rigoll, G. (2018). A deep convolutional neural network for video sequence background subtraction. Pattern Recognition. https://doi.org/10.1016/j.patcog.2017.09.040
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
