Combination of Video Change Detection Algorithms by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Article{Bianco:ieeeTEC,

• author = "Simone Bianco and Gianluigi Ciocca and Raimondo Schettini",
• title = "Combination of Video Change Detection Algorithms by Genetic Programming",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2017",
• volume = "21",
• number = "6",
• pages = "914--928",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Change detection, algorithm combining and selection, CDNET",
• ISSN = "1089-778X",
• URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7898824",
• DOI = "doi:10.1109/TEVC.2017.2694160",
• size = "15 pages",
• abstract = "Within the field of Computer Vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analysing the sequence of frames in a video stream. In this paper we investigate how state-of-the-art change detection algorithms can be combined and used to create a more robust algorithm leveraging their individual peculiarities. We exploited Genetic Programming (GP) to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations on the outputs of the algorithms. In particular, algorithms combination and post-processing operations are achieved with unary, binary and n-ary functions embedded into the GP framework. Using different experimental settings for combining existing algorithms we obtained different GP solutions that we termed IUTIS (In Unity There Is Strength). These solutions are then compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the Change Detection. net (CDNET 2014) challenge. Results demonstrate that using GP, our solutions are able to outperform all the considered single state-of-the-art change detection algorithms, as well as other combination strategies. The performance of our algorithm are significantly different from those of the other state-of-the-art algorithms. This fact is supported by the statistical significance analysis conducted with the Friedman Test and Wilcoxon Rank Sum post-hoc tests.",
• notes = "also known as \cite{7898824} See also \cite{oai:arXiv.org:1505.02921}",

}

Genetic Programming entries for Simone Bianco Gianluigi Ciocca Raimondo Schettini

Citations