video stabilization based on saliency driven sift matching and discriminative ransac
Inspired by the stability functions of human vision system, we present a novel video stabilization method based on saliency driven SIFT matching and discriminative RANSAC. Firstly, a saliency detection method is adopted to estimate the spatial distribution of visual attention degrees in each frame of the video, and the SIFT features are extracted from the salient regions indicated by the saliency map. Then, we further achieve a modified version of RANSAC method using the discriminative features to estimate the trajectory of inter-frame motion and reduce the errors caused by the foreground vector. Finally, Kalman filter is applied to complete the motion smoothing task. Experimental results demonstrate that our approach is efficient and promising compared with state-of-the-art methods.
video stabilization visual attention model discriminative sift feature motion analysis