Background Modeling with Motion Criterion and Multi-modal Support

In this paper we introduce an algorithm aimed to create a background model with multimodal support, which associates a confidence value to the obtained model. Our algorithm creates the model based on a criterion of motion, pixel behavior and pixel similarity with the scenes background. This method uses only three frames to create a first model without restrictions on the frame content. The model is adapted over time to reflect new situations and illumination changes in the scene. One approach to detect corrupt model is also mentioned. The goal of confidence value is to quantify the quality of the model after a number of frames have been used to build it. Quantitative experimental results are obtained with a well-known benchmark and compared to a classical background modelling algorithm, showing the benefits of our approach.