Video Object Segmentation with Multivalued Neural Networks

 

Abstract

The aim of this work is to present a segmentation method to detect moving objects in video scenes, based on the use of a multivalued discrete neural network to improve the results obtained by an underlying segmentation algorithm. Specifically, the multivalued neural model (MREM) is used to detect and correct some of the deficiencies and errors off the well-known Mixture of Gaussians algorithm. Experimental results, using video scenes publicly available from the Internet, show an increase of the visual quality of the segmentation, that could improve for subsequent analysis phases, such as object tracking or behavior studies.

Citation

Please, cite this work as:

[Luq+08] R. M. Luque, D. López-Rodríguez, E. M. Casermeiro, et al. “Video Object Segmentation with Multivalued Neural Networks”. In: 8th International Conference on Hybrid Intelligent Systems (HIS 2008), September 10-12, 2008, Barcelona, Spain. Ed. by F. Xhafa, F. Herrera, A. Abraham, M. Köppen and J. M. Benítez. IEEE Computer Society, 2008, pp. 613-618. DOI: 10.1109/HIS.2008.130. URL: https://doi.org/10.1109/HIS.2008.130.

BibTeX

@inproceedings{DBLP:conf/his/LuqueLCP08,
     author = {Rafael Marcos Luque and
     Domingo López{-}Rodríguez and
     Enrique Mérida Casermeiro and
     Esteban J. Palomo},
     editor = {Fatos Xhafa and
     Francisco Herrera and
     Ajith Abraham and
     Mario K{"{o}}ppen and
     José Manuel Benítez},
     title = {Video Object Segmentation with Multivalued Neural Networks},
     booktitle = {8th International Conference on Hybrid Intelligent Systems {(HIS}
     2008), September 10-12, 2008, Barcelona, Spain},
     pages = {613–618},
     publisher = {{IEEE} Computer Society},
     year = {2008},
     url = {https://doi.org/10.1109/HIS.2008.130},
     doi = {10.1109/HIS.2008.130},
     timestamp = {Wed, 16 Oct 2019 14:14:55 +0200},
     biburl = {https://dblp.org/rec/conf/his/LuqueLCP08.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org}
}

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12

CITATIONS

12 Total citations

0 Recent citations

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Video Object Segmentation with Multivalued Neural Networks

Cites

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Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

[1] T. Bouwmans. “Traditional and recent approaches in background modeling for foreground detection: An overview”. In: Computer Science Review 11–12 (May. 2014), p. 31–66. ISSN: 1574-0137. DOI: 10.1016/j.cosrev.2014.04.001. URL: http://dx.doi.org/10.1016/j.cosrev.2014.04.001.

[2] M. Kaushal, B. S. Khehra, and A. Sharma. “Soft Computing based object detection and tracking approaches: State-of-the-Art survey”. In: Applied Soft Computing 70 (Sep. 2018), p. 423–464. ISSN: 1568-4946. DOI: 10.1016/j.asoc.2018.05.023. URL: http://dx.doi.org/10.1016/j.asoc.2018.05.023.

[3] A. K. S. Kushwaha and R. Srivastava. “Automatic moving object segmentation methods under varying illumination conditions for video data: comparative study, and an improved method”. In: Multimedia Tools and Applications 75.23 (Sep. 2015), p. 16209–16264. ISSN: 1573-7721. DOI: 10.1007/s11042-015-2927-4. URL: http://dx.doi.org/10.1007/s11042-015-2927-4.

[4] M. A. Molina-Cabello, E. López-Rubio, R. M Luque-Baena, et al. “Foreground object detection for video surveillance by fuzzy logic based estimation of pixel illumination states”. In: Logic Journal of the IGPL (Sep. 2018). ISSN: 1368-9894. DOI: 10.1093/jigpal/jzy024. URL: http://dx.doi.org/10.1093/jigpal/jzy024.

[5] R. Moudgollya, A. Midya, A. K. Sunaniya, et al. “Dynamic background modeling using intensity and orientation distribution of video sequence”. In: Multimedia Tools and Applications 78.16 (Apr. 2019), p. 22537–22554. ISSN: 1573-7721. DOI: 10.1007/s11042-019-7575-7. URL: http://dx.doi.org/10.1007/s11042-019-7575-7.

[6] G. Ramirez-Alonso and M. I. Chacon-Murguia. “Object detection in video sequences by a temporal modular self-adaptive SOM”. In: Neural Computing and Applications 27.2 (Mar. 2015), p. 411–430. ISSN: 1433-3058. DOI: 10.1007/s00521-015-1859-2. URL: http://dx.doi.org/10.1007/s00521-015-1859-2.

[7] J. D. Romero, M. J. Lado, and A. J. Mendez. “A Background Modeling and Foreground Detection Algorithm Using Scaling Coefficients Defined With a Color Model Called Lightness-Red-Green-Blue”. In: IEEE Transactions on Image Processing 27.3 (Mar. 2018), p. 1243–1258. ISSN: 1941-0042. DOI: 10.1109/tip.2017.2776742. URL: http://dx.doi.org/10.1109/tip.2017.2776742.

[8] G. Takhar, C. Prakash, N. Mittal, et al. “Comparative analysis of Background Subtraction techniques and applications”. In: 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE, Dec. 2016, p. 1–8. DOI: 10.1109/icraie.2016.7939553. URL: http://dx.doi.org/10.1109/icraie.2016.7939553.

[9] Z. Xue, X. Yuan, and Y. Yang. “Denoising-Based Turbo Message Passing for Compressed Video Background Subtraction”. In: IEEE Transactions on Image Processing 30 (2021), p. 2682–2696. ISSN: 1941-0042. DOI: 10.1109/tip.2021.3055063. URL: http://dx.doi.org/10.1109/tip.2021.3055063.