F1 Score Assesment of Gaussian Mixture Background Subtraction Algorithms Using the MuHAVi Dataset
F1 Score Assesment of Gaussian Mixture Background Subtraction Algorithms Using the MuHAVi Dataset
- Author(s): J. Sepúlveda and S.A. Velastin
- DOI: 10.1049/ic.2015.0106
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- Author(s): J. Sepúlveda and S.A. Velastin Source: 6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15), 2015 page ()
- Conference: 6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15)
- DOI: 10.1049/ic.2015.0106
- ISBN: 978-1-78561-131-5
- Location: London, UK
- Conference date: 15-17 July 2015
- Format: PDF
Background subtraction algorithms are mainly used to segment some specific moving objects in an image sequences. Within of the action recognition context, these methods may be proper to generate automatically silhouettes of the human actions. In this way, MuHAVi is a human action dataset which provides a small set of manually annotated silhouettes and a large set of multicamera raw video. The purpose of this work is to use a segmentation algorithm to generate automatically the whole dataset of silhouettes for the MuHAVi raw video. The F1-score unit measurement is the selection criterion as for the best method to generate such silhouettes. This paper focuses especially on background subtraction methods that create a statistical model of the background, typically using a mixture of Gaussian. The best-evaluated algorithm can then be used to generate automatically a set of silhouettes.
Inspec keywords: Gaussian processes; image motion analysis; image sensors; image sequences; video signal processing
Subjects: Video signal processing; Computer vision and image processing techniques; Optical, image and video signal processing; Other topics in statistics; Image sensors; Other topics in statistics
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