Abstract
A frame resolution reduction framework to reduce the computational load and improve the foreground detection in video sequences is presented in this work. The proposed framework consists of three different stages. Firstly, the original video frame is downsampled using a specific interpolation function. Secondly, a foreground detection of the reduced video frame is performed by a probabilistic background model called MFBM. Finally, the class probabilities for the reduced video frame are upsampled using a bicubic interpolation to estimate the class probabilities of the original frame. Experimental results applied to standard benchmark video sequences demonstrate the goodness of our proposal.
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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 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. Finally, it is partially supported by the Autonomous Government of Extremadura (Spain) under the project IB13113. 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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Molina-Cabello, M.A., López-Rubio, E., Luque-Baena, R.M., Palomo, E.J., Domínguez, E. (2016). Frame Size Reduction for Foreground Detection in Video Sequences. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_1
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DOI: https://doi.org/10.1007/978-3-319-44636-3_1
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