Illumination-based Data Augmentation for Robust Background Subtraction

Dimitrios Sakkos, Hubert P. H. Shum and Edmond S. L. Ho
Proceedings of the 2019 International Conference on Software Knowledge Information Management and Applications (SKIMA), 2019

Citation: 7## Best Paper Award

Illumination-based Data Augmentation for Robust Background Subtraction
## Citation counts from Google Scholar as of 2022

Abstract

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In this paper, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly, but also features semantic transformations of illumination which enhance the generalisation of the model. It successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. Such data allows us to effectively train an illumination-invariant deep learning model for BGS. Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.

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BibTeX

@inproceedings{sakkos19triplet,
 author={Sakkos, Dimitrios and Shum, Hubert P. H. and Ho, Edmond S. L.},
 booktitle={Proceedings of the 2018 International Conference on Software Knowledge Information Management and Applications},
 series={SKIMA '19},
 title={Illumination-based Data Augmentation for Robust Background Subtraction},
 year={2019},
 month={Aug},
 pages={1--8},
 numpages={8},
 doi={10.1109/SKIMA47702.2019.8982527},
 publisher={IEEE},
 location={Island of Ukulhas, Maldives},
}

RIS

TY  - CONF
AU  - Sakkos, Dimitrios
AU  - Shum, Hubert P. H.
AU  - Ho, Edmond S. L.
T2  - Proceedings of the 2018 International Conference on Software Knowledge Information Management and Applications
TI  - Illumination-based Data Augmentation for Robust Background Subtraction
PY  - 2019
Y1  - Aug 2019
SP  - 1
EP  - 8
DO  - 10.1109/SKIMA47702.2019.8982527
PB  - IEEE
ER  - 

Plain Text

Dimitrios Sakkos, Hubert P. H. Shum and Edmond S. L. Ho, "Illumination-based Data Augmentation for Robust Background Subtraction," in SKIMA '19: Proceedings of the 2018 International Conference on Software Knowledge Information Management and Applications, pp. 1-8, Island of Ukulhas, Maldives, IEEE, Aug 2019.

Similar Research

Dimitrios Sakkos, Edmond S. L. Ho, Hubert P. H. Shum and Garry Elvin, "Image Editing based Data Augmentation for Illumination-insensitive Background Subtraction", Journal of Enterprise Information Management (JEIM), 2020
Dimitrios Sakkos, Edmond S. L. Ho and Hubert P. H. Shum, "Illumination-aware Multi-task GANs for Foreground Segmentation", IEEE Access, 2019

 

 
 

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