Moving object detection is a critical task for many computer vision applications: the objective is the classification of the pixels in the video sequence into either foreground or background. A commonly used technique to achieve it in scenes captured by a static camera is Background Subtraction (BGS). Several BGS techniques have been proposed in the literature but a rigorous comparison that analyzes the different parameter configuration for each technique in different scenarios with precise ground-truth data is still lacking. In this sense, we have implemented and evaluated the most relevant BGS techniques, and performed a quantitative and qualitative comparison between them. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Herrero, S., & Bescós, J. (2009). Background subtraction techniques: Systematic evaluation and comparative analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5807 LNCS, pp. 33–42). https://doi.org/10.1007/978-3-642-04697-1_4
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