  Formulating the multi object tracking problem as a network flow optimization
problem is a popular choice. In this paper an efficient way of learning the
weights of such a network is presented. It separates the problem into one
embedding of feasible solutions into a one dimensional feature space and one
optimization problem. The embedding can be learned using standard SGD type
optimization without relying on an additional optimizations within each step.
Training data is produced by performing small perturbations of ground truth
tracks and representing them using generalized graph differences, which is an
efficient way introduced to represent the difference between two graphs. The
proposed method is evaluated on DukeMTMCT with competitive results.
