  We describe an end-to-end framework for learning parameters of min-cost flow
multi-target tracking problem with quadratic trajectory interactions including
suppression of overlapping tracks and contextual cues about cooccurrence of
different objects. Our approach utilizes structured prediction with a
tracking-specific loss function to learn the complete set of model parameters.
In this learning framework, we evaluate two different approaches to finding an
optimal set of tracks under a quadratic model objective, one based on an LP
relaxation and the other based on novel greedy variants of dynamic programming
that handle pairwise interactions. We find the greedy algorithms achieve almost
equivalent accuracy to the LP relaxation while being up to 10x faster than a
commercial LP solver. We evaluate trained models on three challenging
benchmarks. Surprisingly, we find that with proper parameter learning, our
simple data association model without explicit appearance/motion reasoning is
able to achieve comparable or better accuracy than many state-of-the-art
methods that use far more complex motion features or appearance affinity metric
learning.
