Noise Reduction using MRF and Block-Based Background Modeling in Dynamic Scenes Input
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Int Journal Computer Science & Network Security-Ijcsns
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Identifying the moving foreground object in dynamic scenes and making the analysis of video sequences accurate and powerful is an important process for video surveillance systems. Environmental factors such as environmental noises and sudden light changes are the main factors of the degradation of the background model. Complex algorithms are needed to create a strong background against these factors. In this study, We increased the noise immunity of the background model exposed to environmental noise by applying Markov random field (MRF) to block-based modified KDE (Kernel Density Estimation). We also reduced the storage space requirement with the KDE structure we created in blocks. Thus we have increased the applicability of this structure to a real-time structure.
Açıklama
Anahtar Kelimeler
Kernel Density Estimation, Markov random field, Background modeling, Adaptive threshold parameter
Kaynak
International Journal of Computer Science and Network Security
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
17
Sayı
1
