numerous algorithms have been proposed for object tracking with demonstrated
success, it remains a challenging problem for a tracker to handle large change
in scale, motion, shape deformation with occlusion.
One of the main reasons is the lack of effective image representation to
account for appearance variation. Most trackers use high-level appearance
structure or low-level cues for representing and matching target objects. In
this paper, we propose a tracking method from the perspective of mid-level
vision with structural information captured in superpixels.
We present a discriminative appearance model based on superpixels,
thereby facilitating a tracker to distinguish the target and the background
with mid-level cues. The tracking task is then formulated by computing a
target-background confidence map, and obtaining the best candidate by maximum a
posterior estimate. Experimental results demonstrate that our tracker is able
to handle heavy occlusion and recover from drifts. In conjunction with online
update, the proposed algorithm is shown to perform favorably against existing
methods for object tracking.
Code and data set