Background modeling by shifted tilings of stacked denoising autoencoders
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Autores: Jorge García González, Juan Miguel Ortiz de Lazcano Lobato
, Rafael M. Luque Baena , Ezequiel López Rubio
- Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.) , José Ramón Álvarez Sánchez (dir. congr.) , Félix de la Paz López (dir. congr.) , Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 307-316
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
The effective processing of visual data without interruptionis currently of supreme importance. For that purpose, the analysis system must adapt to events that may affect the data quality and maintain its performance level over time. A methodology for background modeling and foreground detection, whose main characteristic is its robustness against stationary noise, is presented in the paper. The system is based on a stacked denoising autoencoder which extracts a set of significant features for each patch of several shifted tilings of the video frame.A probabilistic model for each patch is learned. The distinct patches which include a particular pixel are considered for that pixel classification.The experiments show that classical methods existing in the literature experience drastic performance drops when noise is present in the video sequences, whereas the proposed one seems to be slightly affected.This fact corroborates the idea of robustness of our proposal, in addition to its usefulness for the processing and analysis of continuous data during uninterrupted periods of time.
