Visual saliency detection using video decomposition

Estimation of salient regions in an input video is an active area of research due to its wide applications. In this paper, we propose a novel algorithm to estimate the eye gaze movement in a video using motion, color and structural cues with minimum outliers. The algorithm is generalized to capture salient information for the videos taken under different camera motions. The entire algorithm is parallelizable and ensures faster estimation of salient regions. Using different standard datasets, the estimations of proposed algorithm are compared with state-of-the-art approaches. It is observed that the proposed method produces estimations closer to the ground-truth eye tracker data with minimum outliers.