This paper presents a video stabilization algorithm based on the extraction and tracking of Scale Invariant Feature Transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of Iterative Least Squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with Adaptive Motion Vector Integration. Results confirm the effectiveness of the method.

SIFT Features Tracking for Video Stabilization

BATTIATO, SEBASTIANO;GALLO, Giovanni;
2007-01-01

Abstract

This paper presents a video stabilization algorithm based on the extraction and tracking of Scale Invariant Feature Transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of Iterative Least Squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with Adaptive Motion Vector Integration. Results confirm the effectiveness of the method.
2007
978-076952877-9
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/20.500.11769/91644
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 201
  • ???jsp.display-item.citation.isi??? 128
social impact