  Traditionally, object tracking and segmentation are treated as two separate
problems and solved independently. However, in this paper, we argue that
tracking and segmentation are actually closely related and solving one should
help the other. On one hand, the object track, which is a set of bounding boxes
with one bounding box in every frame, would provide strong high-level guidance
for the target/background segmentation task. On the other hand, the object
segmentation would separate object from other objects and background, which
will be useful for determining track locations in every frame. We propose a
novel framework which combines online multiple target tracking and segmentation
in a video. In our approach, the tracking and segmentation problems are coupled
by Lagrange dual decomposition, which leads to more accurate segmentation
results and also \emph{helps resolve typical difficulties in multiple target
tracking, such as occlusion handling, ID-switch and track drifting}. To track
targets, an individual appearance model is learned for each target via
structured learning and network flow is employed to generate tracks from
densely sampled candidates. For segmentation, multi-label Conditional Random
Field (CRF) is applied to a superpixel based spatio-temporal graph in a segment
of video to assign background or target labels to every superpixel. The
experiments on diverse sequences show that our method outperforms
state-of-the-art approaches for multiple target tracking as well as
segmentation.
