Background updating is fundamental in mobile objects detection applications. This paper proposes a background updating method with a moving stereo camera. The proposed algorithm is based on the detection of the regions in the image that have major color intensity in the scene (called light zones). From these light zones some keypoints are extracted and matched between the previous background and the current foreground images. Image registration is performed by moving the old background image according to the keypoints matching so that the foreground and background images are mostly aligned. The proposed method requires that the camera moves slowly and it is used for moving objects detection with background subtraction. Three types of keypoints are tested using the same homography: light zone, SIFT and SURF keypoints. We show experimentally that, on the average, light zone keypoints performances are equal to or better than SIFT keypoints, and are faster to compute; moreover, the SURF keypoints perform worse. To get better performances, when the light zone keypoints fail, then the SIFT keypoints are used in a data fusion ramework.

Dynamic background modeling for moving objects detection using a mobile stereo camera

MUMOLO, ENZO;Massimiliano, Nolich;
2010

Abstract

Background updating is fundamental in mobile objects detection applications. This paper proposes a background updating method with a moving stereo camera. The proposed algorithm is based on the detection of the regions in the image that have major color intensity in the scene (called light zones). From these light zones some keypoints are extracted and matched between the previous background and the current foreground images. Image registration is performed by moving the old background image according to the keypoints matching so that the foreground and background images are mostly aligned. The proposed method requires that the camera moves slowly and it is used for moving objects detection with background subtraction. Three types of keypoints are tested using the same homography: light zone, SIFT and SURF keypoints. We show experimentally that, on the average, light zone keypoints performances are equal to or better than SIFT keypoints, and are faster to compute; moreover, the SURF keypoints perform worse. To get better performances, when the light zone keypoints fail, then the SIFT keypoints are used in a data fusion ramework.
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: http://hdl.handle.net/11368/2843576
 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??? ND
social impact