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Common users have changed from mere consumers to active producers ofmultimedia data content. Video editing plays an important role in thisscenario, calling for simple segmentation tools that can handle fast-moving anddeformable video objects with possible occlusions, color similarities with thebackground, among other challenges. We present an interactive videosegmentation method, named FOMTrace, which addresses the problem in aneffective and efficient way. From a user-provided object mask in a first frame,the method performs semi-automatic video segmentation on a spatiotemporalsuperpixel-graph, and then estimates a Fuzzy Object Model (FOM), which refinessegmentation of the second frame by constraining delineation on a pixel-graphwithin a region where the object's boundary is expected to be. The user cancorrect/accept the refined object mask in the second frame, which is thensimilarly used to improve the spatiotemporal video segmentation of theremaining frames. Both steps are repeated alternately, within interactiveresponse times, until the segmentation refinement of the final frame isaccepted by the user. Extensive experiments demonstrate FOMTrace's ability fortracing objects in comparison with state-of-the-art approaches for interactivevideo segmentation, supervised, and unsupervised object tracking.

FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models