Abstract

Multi-object tracking has been recently approached with the min-cost network flflow optimization techniques. Such methods simultaneously resolve multiple object tracks in a video and enable modeling of dependencies among tracks. Min-cost network flflow methods also fifit 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 flflow framework. While integer solutions to such a problem become NP-hard, we design a convex relaxation solution with an effificient rounding heuristic which empirically gives certifificates of small suboptimality. We evaluate two particular types of pairwise costs and demonstrate improvements over recent tracking methods in real-world video sequences