Abstract
Most of video-surveillance based applications use a foreground extraction algorithm to detect interest objects from videos provided by static cameras. This paper presents a benchmark dataset and evaluation process built from both synthetic and real videos, used in the BMC workshop (Background Models Challenge). This dataset focuses on outdoor situations with weather variations such as wind, sun or rain. Moreover, we propose some evaluation criteria and an associated free software to compute them from several challenging testing videos. The evaluation process has been applied for several state of the art algorithms like gaussian mixture models or codebooks. © 2013 Springer-Verlag.
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CITATION STYLE
Vacavant, A., Chateau, T., Wilhelm, A., & Lequièvre, L. (2013). A benchmark dataset for outdoor foreground/background extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7728 LNCS, pp. 291–300). https://doi.org/10.1007/978-3-642-37410-4_25
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