Projects | Deep Network Flow for Multi-Object Tracking

CVPR 2017 | We demonstrate that it is possible to learn features for network-flow-based data association via backpropagation by expressing the optimum of a smoothed network flow problem as a differentiable function of the pairwise association costs. We apply this approach to multi-object tracking with a network-flow formulation. Our experiments demonstrate that we are able to successfully learn all cost functions for the association problem in an end-to-end fashion, outperforming hand-crafted costs in all settings. The integration and combination of various sources of inputs become easy, and the cost functions can be learned entirely from data, alleviating tedious hand-designing of costs.