One of the most challenging tasks in computer vision is to emulate human cognitive ability to extract the salient object in a scene. We tackle the task of unsupervised salient video object segmentation using boundary connectedness and space-time salient regions. First, boundary prior measure is used to separate salient regions detected in both space and time. Then, background-foreground regions connectedness is computed and combined with appearance model via an iterative energy minimization framework to segment the salient moving object. For temporal consistency, the segmentation result of the current frame is used in addition to the optical flow and the boundary prior to segmenting the next frame. The experiments show a good performance of our algorithm for salient video object segmentation on benchmark datasets even in the presence of different challenges.
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.
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