Video stabilization using principal component analysis and scale invariant feature transform in particle filter framework

This paper presents a novel approach to digital video stabilization that uses adaptive particle filter for global motion estimation. In this approach, dimensionality of the feature space is first reduced by the principal component analysis (PCA) method using the features obtained from a scale invariant feature transform (SIFT), and hence the resultant features may be termed as the PCA-SIFT features. The trajectory of these features extracted from video frames is used to estimate undesirable motion between frames. A new cost function called SIFT-BMSE (SIFT Block Mean Square Error) is proposed in adaptive particle filter framework to disregard the foreground object pixels and reduce the computational cost. Frame compensation based on these estimates yields stabilized full-frame video sequences. Experimental results show that the proposed algorithm is both accurate and efficient.