Deep Network Flow for Multi-Object Tracking

Understand

Data association problems are an important component of many computer vision applications, with multi-object tracking being one of the most prominent examples.

• A typical approach to data association involves finding a graph matching or network flow that minimizes a sum of pairwise association costs, which are often either hand-crafted or learned as linear functions of fixed features.
• In this work, 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.