Robust segmentation of moving objects in video based on spatiotemporal visual saliency and active contour model

Abstract

This paper presents an algorithm for automatic segmentation of moving objects in video based on spatiotemporal visual saliency and an active contour model. Our algorithm exploits the visual saliency and motion information to build a spatiotemporal visual saliency map used to extract a moving region of interest. This region is used to automatically provide the seeds for the convex active contour (CAC) model to segment the moving object accurately. The experiments show a good performance of our algorithm for moving object segmentation in video without user interaction, especially on the SegTrack dataset.