  This paper concerns the problem of multi-object tracking based on the
min-cost flow (MCF) formulation, which is conventionally studied as an instance
of linear program. Given its computationally tractable inference, the success
of MCF tracking largely relies on the learned cost function of underlying
linear program. Most previous studies focus on learning the cost function by
only taking into account two frames during training, therefore the learned cost
function is sub-optimal for MCF where a multi-frame data association must be
considered during inference. In order to address this problem, in this paper we
propose a novel differentiable framework that ties training and inference
together during learning by solving a bi-level optimization problem, where the
lower-level solves a linear program and the upper-level contains a loss
function that incorporates global tracking result. By back-propagating the loss
through differentiable layers via gradient descent, the globally parameterized
cost function is explicitly learned and regularized. With this approach, we are
able to learn a better objective for global MCF tracking. As a result, we
achieve competitive performances compared to the current state-of-the-art
methods on the popular multi-object tracking benchmarks such as MOT16, MOT17
and MOT20.
