The real time detection of moving objects in video sequences has many important applications . This paper proposes the FPGA hardware implementation of segmentation and denoising units. The segmentation is conducted using the Gaussian Mixture Model (GMM), a probabilistic method for the moving objects identification. The implemented algorithm is the OpenCV (Open source Computer Vision library) version of the GMM. The denoising is conducted implementing the morphological operators of erosion, dilation, opening and closing. The proposed HW design achieves real time processing of HD (High Definition, frame size 1920x1080) video sequences.

FPGA architecture for real time video segmentation and denoising

E. Napoli;
2012-01-01

Abstract

The real time detection of moving objects in video sequences has many important applications . This paper proposes the FPGA hardware implementation of segmentation and denoising units. The segmentation is conducted using the Gaussian Mixture Model (GMM), a probabilistic method for the moving objects identification. The implemented algorithm is the OpenCV (Open source Computer Vision library) version of the GMM. The denoising is conducted implementing the morphological operators of erosion, dilation, opening and closing. The proposed HW design achieves real time processing of HD (High Definition, frame size 1920x1080) video sequences.
2012
9788867410125
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4772723
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 45
social impact