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Memetic Computing
Erschienen in: Memetic Computing 1/2018

08.02.2017 | Regular Research Paper

Improved visual background extractor with adaptive range change

verfasst von: Shiyu Yang, Kuangrong Hao, Yongsheng Ding, Jian Liu

Erschienen in: Memetic Computing | Ausgabe 1/2018

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Abstract

The visual background extractor (ViBe) has become one of the best motion object detection algorithms because of its good detection results and low memory requirements. However, the ViBe model cannot self-adjust the value range of the parameter that controls the number of samples chosen from the background template. In this paper, two models are proposed to help automatically change the parameter range in different environments. The blink energy model can detect dynamic backgrounds by increasing the range, while the object probability model can prevent corrosion of motion objects by decreasing the range. The experimental results show that our proposed method can both accurately recognize dynamic backgrounds and efficiently prevent object corrosion. In addition, our method shows better performance on benchmark datasets than several commonly used detection algorithms.
Vorheriger Artikel Kernel online sequential ELM algorithm with sliding window subject to time-varying environments
Nächster Artikel EDDE–LNS: a new hybrid ensemblist approach for feature selection

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Metadaten
Titel
Improved visual background extractor with adaptive range change
verfasst von
Shiyu Yang
Kuangrong Hao
Yongsheng Ding
Jian Liu
Publikationsdatum
08.02.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 1/2018
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-017-0225-6

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