Video Background Subtraction in Complex Environments

 

Juana E. Santoyo-Morales and Rogelio Hasimoto-Beltrán

 

Centro de Investigación en Matemáticas-CIMAT, Jalisco s/n, Col. Mineral de Valenciana, Guanajuato, Gto., México 36240. {juanita,hasimoto}@cimat.mx

 

ABSTRACT

Background subtraction models based on mixture of Gaussians have been extensively used for detecting objects in motion in a wide variety of computer vision applications. However, background subtraction modeling is still an open problem particularly in video scenes with drastic illumination changes and dynamic backgrounds (complex backgrounds). The purpose of the present work is focused on increasing the robustness of background subtraction models to complex environments. For this, we proposed the following enhancements: a) redefine the model distribution parameters involved in the detection of moving objects (distribution weight, mean and variance), b) improve pixel classification (background/foreground) and variable update mechanism by a new time-space dependent learning-rate parameter, and c) replace the pixel-based modeling currently used in the literature by a new space-time region-based model that eliminates the noise effect caused by drastic changes in illumination. Our proposed scheme can be implemented on any state of the art background subtraction scheme based on mixture of Gaussians to improve its resilient to complex backgrounds. Experimental results show excellent noise removal and object motion detection properties under complex environments.

Keywords: Background subtraction, Mixture of Gaussians, Expectation-Maximization Method.

 

RESUMEN

Los modelos de substracción de fondo basados en mezcla de Gaussianas han sido ampliamente usados para la detección de objetos en movimiento en diversas aplicaciones de visión computacional. Sin embargo, la substracción de fondo sigue siendo un problema abierto, particularmente en escenas de video donde existen cambios drásticos de iluminación y fondo dinámico. El presente trabajo tiene por objetivo incrementar la robustez de los modelos de substracción de fondo en ambientes complejos, para esto se propone: a) redefinir los parámetros de la distribución de mezclas que afectan la detección de objetos en movimiento (peso, media y varianza de la distribución); b) mejorar la clasificación de pixels (fondo/objeto) y el mecanismo de actualización de las variables mediante la aplicación de un nuevo parámetro de velocidad de aprendizaje que depende de la historia temporal y espacial de los objetos en movimiento c) reemplazar el modelo de substracción de fondo a nivel de pixel usado actualmente por un modelo que cubre una región espacio-temporal para la eliminación de ruido causado por cambios drásticos de iluminación. Las propuestas pueden ser implementadas en cualquier esquema de sustracción de fondo basado en mezcla de Gaussianas para mejorar su respuesta en situaciones de fondos complejos. Resultados experimentales del modelo muestran su excelente capacidad para la eliminación de ruido y detección de objetos en movimiento en ambientes de fondo complejo.

 

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