  Common users have changed from mere consumers to active producers of
multimedia data content. Video editing plays an important role in this
scenario, calling for simple segmentation tools that can handle fast-moving and
deformable video objects with possible occlusions, color similarities with the
background, among other challenges. We present an interactive video
segmentation method, named FOMTrace, which addresses the problem in an
effective and efficient way. From a user-provided object mask in a first frame,
the method performs semi-automatic video segmentation on a spatiotemporal
superpixel-graph, and then estimates a Fuzzy Object Model (FOM), which refines
segmentation of the second frame by constraining delineation on a pixel-graph
within a region where the object's boundary is expected to be. The user can
correct/accept the refined object mask in the second frame, which is then
similarly used to improve the spatiotemporal video segmentation of the
remaining frames. Both steps are repeated alternately, within interactive
response times, until the segmentation refinement of the final frame is
accepted by the user. Extensive experiments demonstrate FOMTrace's ability for
tracing objects in comparison with state-of-the-art approaches for interactive
video segmentation, supervised, and unsupervised object tracking.
