Given the existence of many change detection algorithms, each with its own peculiarities and strengths, we propose a combination strategy, that we termed IUTIS (In Unity There Is Strength), based on a genetic Programming framework. This combination strategy is aimed at leveraging the strengths of the algorithms and compensate for their weakness. In this paper we show our findings in applying the proposed strategy in two different scenarios. The first scenario is purely performance-based. The second scenario performance and efficiency must be balanced. Results demonstrate that starting from simple algorithms we can achieve comparable results with respect to more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications.

Bianco, S., Ciocca, G., Schettini, R. (2017). How far can you get by combining change detection algorithms?. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.96-107). Springer Verlag [10.1007/978-3-319-68560-1_9].

How far can you get by combining change detection algorithms?

Bianco, S;Ciocca, G
;
Schettini, R.
2017

Abstract

Given the existence of many change detection algorithms, each with its own peculiarities and strengths, we propose a combination strategy, that we termed IUTIS (In Unity There Is Strength), based on a genetic Programming framework. This combination strategy is aimed at leveraging the strengths of the algorithms and compensate for their weakness. In this paper we show our findings in applying the proposed strategy in two different scenarios. The first scenario is purely performance-based. The second scenario performance and efficiency must be balanced. Results demonstrate that starting from simple algorithms we can achieve comparable results with respect to more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications.
paper
Algorithm combining and selection; CDNET; Change detection; Genetic programming; Video surveillance; Theoretical Computer Science; Computer Science (all)
English
International Conference on Image Analysis and Processing, ICIAP 2017 - 11-15 september
2017
IMAGE ANALYSIS AND PROCESSING,(ICIAP 2017), PT I
Battiato, S; Gallo, G; Schettini, R; Stanco, F
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9783319685595
2017
10484
96
107
http://springerlink.com/content/0302-9743/copyright/2005/
none
Bianco, S., Ciocca, G., Schettini, R. (2017). How far can you get by combining change detection algorithms?. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.96-107). Springer Verlag [10.1007/978-3-319-68560-1_9].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/176510
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