  Moving Object Segmentation is a challenging task for jittery/wobbly videos.
For jittery videos, the non-smooth camera motion makes discrimination between
foreground objects and background layers hard to solve. While most recent works
for moving video object segmentation fail in this scenario, our method
generates an accurate segmentation of a single moving object. The proposed
method performs a sparse segmentation, where frame-wise labels are assigned
only to trajectory coordinates, followed by the pixel-wise labeling of frames.
The sparse segmentation involving stabilization and clustering of trajectories
in a 3-stage iterative process. At the 1st stage, the trajectories are
clustered using pairwise Procrustes distance as a cue for creating an affinity
matrix. The 2nd stage performs a block-wise Procrustes analysis of the
trajectories and estimates Frechet means (in Kendall's shape space) of the
clusters. The Frechet means represent the average trajectories of the motion
clusters. An optimization function has been formulated to stabilize the Frechet
means, yielding stabilized trajectories at the 3rd stage. The accuracy of the
motion clusters are iteratively refined, producing distinct groups of
stabilized trajectories. Next, the labels obtained from the sparse segmentation
are propagated for pixel-wise labeling of the frames, using a GraphCut based
energy formulation. Use of Procrustes analysis and energy minimization in
Kendall's shape space for moving object segmentation in jittery videos, is the
novelty of this work. Second contribution comes from experiments performed on a
dataset formed of 20 real-world natural jittery videos, with manually annotated
ground truth. Experiments are done with controlled levels of artificial jitter
on videos of SegTrack2 dataset. Qualitative and quantitative results indicate
the superiority of the proposed method.
