How Far Can You Get By Combining Change Detection Algorithms?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2015.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 2.
6 papers.
- 2021BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtractioncited by 15
- 2021Universal Background Subtraction based on Arithmetic Distribution Neural Networkcited by 11
- 2021Autoencoder-based background reconstruction and foreground segmentation with background noise estimationcited by 7
- 2023Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentationcited by 4
- 2022Dynamic Background Subtraction by Generative Neural Networkscited by 3
- 2023Weakly Supervised Realtime Dynamic Background Subtractioncited by 0
- 2023Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation19 Apr 2023
- 2023Weakly Supervised Realtime Dynamic Background Subtraction6 Mar 2023
- 2022Dynamic Background Subtraction by Generative Neural Networks10 Feb 2022
- 2021Autoencoder-based background reconstruction and foreground segmentation with background noise estimation15 Dec 2021
- 2021Universal Background Subtraction based on Arithmetic Distribution Neural Network16 Apr 2021
- 2021BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction23 Jan 2021