RT Journal Article
T1 Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences
A1 García-González, Jorge
A1 Ortiz-de-Lazcano-Lobato, Juan Miguel
A1 Luque-Baena, Rafael Marcos
A1 Molina-Cabello, Miguel Ángel
A1 López-Rubio, Ezequiel
K1 Ruido - Detección automática
K1 Vídeo
AB A robust foreground detection system is presented, which is resilient to noise in video sequences. The proposed model divides each video frame in patches that are fed to a stacked denoising autoencoder, which is responsible for the extraction of significant features from each image patch. After that, a probabilistic model that is composed of a mixture of Gaussian distributions decides whether the given feature vector describes a patch belonging to the background or the foreground. In order to test the model robustness, several trials with noise of different types and intensities have been carried out. A comparison with other ten state of the art foreground detection algorithms has been drawn. The algorithms have been ranked according to the obtained results, and our proposal appears among the first three positions in most case and its the one that best performs on average.
YR 2019
FD 2019-06-07
LK https://hdl.handle.net/10630/29697
UL https://hdl.handle.net/10630/29697
LA eng
NO García-González, J., Ortiz-de-Lazcano-Lobato, J. M., Luque-Baena, R. M., Molina-Cabello, M. A., & López-Rubio, E. (2019). Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences. Pattern Recognition Letters, 125, 481–487.
NO Licencia CC BY-NC-ND.Versión definitiva disponible en el DOI indicado.García-González, J., Ortiz-de-Lazcano-Lobato, J. M., Luque-Baena, R. M., Molina-Cabello, M. A., & López-Rubio, E. (2019). Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences. Pattern Recognition Letters, 125, 481-487.
NO This work is partially supported by the Ministry of Science, Innovation and Universities of Spain [grant number RTI2018-094645-B-I00], project name Automated detection with low cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) [grant number TIC-657], project name Self-organizing systems and robust estimators for video surveillance. Both 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. They have also been supported by the Biomedic Research Institute of Málaga (IBIMA). They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026