Combination of Video Change Detection Algorithms by Genetic Programming

Within the field of computer vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analyzing 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 cr...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 21; číslo 6; s. 914 - 928
Hlavní autoři: Bianco, Simone, Ciocca, Gianluigi, Schettini, Raimondo
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.12.2017
Témata:
Algorithm combining and selection
Algorithm design and analysis
change detection
Change detection algorithms
ChangeDetection.net (CDNET)
Detection algorithms
Evolutionary computation
Genetic programming
genetic programming (GP)
Robustness
Streaming media
ISSN:1089-778X, 1941-0026
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Shrnutí:Within the field of computer vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analyzing 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 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 ChangeDetection.net 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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2694160