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SFO Detection

This site contains some supplementary material associated to the detection strategy proposed in [*].


The work [*] proposes an efficient and high-quality strategy to detect stationary foreground objects, which is able to detect not only completely static objects but also partially static ones. Three parallel nonparametric detectors with different absorption rates are used to detect currently moving foreground objects, \mbox{short-term} stationary foreground objects, and long-term stationary foreground objects. The results of the detectors are fed into a novel Finite State Machine that classifies the pixels among background, moving foreground objects, stationary foreground objects, occluded stationary foreground objects, and uncovered background. Results show that the proposed detection strategy is not only able to achieve high quality in several challenging situations but it also improves upon previous strategies. For any question about the article [*] or about the described test data, please contact Carlos Cuevas at Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo..

[*] C. Cuevas, R. Martínez, D. Berjón, and N. García, "Detection of stationary foreground objects using multiple nonparametric background-foreground models on a Finite State Machine", IEEE Transactions on Image Processing, vol. xx, no. x, pp. xxx-xxx, 2017 (doi: 10.1109/TIP.2016.2642779) (online since 21-Dec-2016).

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