  Multi-object tracking has been recently approached with the min-cost network
flow optimization techniques. Such methods simultaneously resolve multiple
object tracks in a video and enable modeling of dependencies among tracks.
Min-cost network flow methods also fit well within the "tracking-by-detection"
paradigm where object trajectories are obtained by connecting per-frame outputs
of an object detector. Object detectors, however, often fail due to occlusions
and clutter in the video. To cope with such situations, we propose to add
pairwise costs to the min-cost network flow framework. While integer solutions
to such a problem become NP-hard, we design a convex relaxation solution with
an efficient rounding heuristic which empirically gives certificates of small
suboptimality. We evaluate two particular types of pairwise costs and
demonstrate improvements over recent tracking methods in real-world video
sequences.
