2026-06-01T13:30:41Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/296972026-02-03T11:03:53Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc García-González, Jorge author Ortiz-de-Lazcano-Lobato, Juan Miguel author Luque-Baena, Rafael Marcos author Molina-Cabello, Miguel Ángel author López-Rubio, Ezequiel author 2019-06-07 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. 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. https://hdl.handle.net/10630/29697 10.1016/j.patrec.2019.06.006 Ruido - Detección automática Vídeo Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences