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Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences

2019; Elsevier BV; Volume: 125; Linguagem: Inglês

10.1016/j.patrec.2019.06.006

1872-7344

Jorge García-González, Juan Miguel Ortiz-de-Lazcano-Lobato, Rafael Marcos Luque‐Baena, Miguel A. Molina‐Cabello, Ezequiel López‐Rubio,

Advanced Vision and Imaging

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.