Globally Optimal Multi-target Tracking on a Hexagonal Lattice

We propose a global optimisation approach to multi-target tracking. The method extends recent work which casts tracking as an integer linear program, by discretising the space of target locations. Our main contribution is to show how dynamic models can be integrated in such an approach. The dynamic model, which encodes prior expectations about object motion, has been an important component of tracking systems for a long time, but has recently been dropped to achieve globally optimisable objective functions. We re-introduce it by formulating the optimisation problem such that deviations from the prior can be measured independently for each variable. Furthermore, we propose to sample the location space on a hexagonal lattice to achieve smoother, more accurate trajectories in spite of the discrete setting. Finally, we argue that non-maxima suppression in the measured evidence should be performed during tracking, when the temporal context and the motion prior are available, rather than as a preprocessing step on a per-frame basis. Experiments on five different recent benchmark sequences demonstrate the validity of our approach.