Abstract

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 ob ject motion, has been an important component of tracking sys- tems for a long time, but has recently been dropped to achieve globally optimisable ob jective functions. We re-introduce it by formulating the optimisation problem such that deviations from the prior can be mea- sured independently for each variable. Furthermore, we propose to sam- ple the location space on a hexagonal lattice to achieve smoother, more accurate tra jectories in spite of the discrete setting. Finally, we argue that non-maxima suppression in the measured evidence should be per- formed 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.