Abstract
Detecting foreground objects on scenes is a fundamental task in computer vision and the used color space is an important election for this task. In many situations, especially on dynamic backgrounds, neither grayscale nor RGB color spaces represent the best solution to detect foreground objects. Other standard color spaces, such as YCbCr or HSV, have been proposed for background modeling in the literature; although the best results have been achieved using diverse color spaces according to the application, scene, algorithm, etc. In this work, a color space and a color component weighting selection process are proposed to detect foreground objects in video sequences using self-organizing maps. Experimental results are also provided using well known benchmark videos.
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Acknowledgments
This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2014-53465-R, project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Extremadura (Spain) under the project IB13113 and by the Autonomous Government of Andalusia (Spain) under projects TIC-6213, project name Development of Self-Organizing Neural Networks for Information Technologies; and TIC-657, project name Self-organizing systems and robust estimators for video surveillance. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga.
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López-Rubio, F.J., Domínguez, E., Palomo, E.J. et al. Selecting the Color Space for Self-Organizing Map Based Foreground Detection in Video. Neural Process Lett 43, 345–361 (2016). https://doi.org/10.1007/s11063-015-9431-8
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DOI: https://doi.org/10.1007/s11063-015-9431-8