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.

本文提出了一个可微分的框架，通过解决一个双层优化问题，在学习过程中将训练和推断绑定在一起，以显式学习和正则化全局参数化的代价函数以实现更好的全局 MCF 跟踪目标，从而在 MOT16，MOT17 和 MOT20 等流行的多对象跟踪基准测试中实现了与当前最先进方法相当的竞争表现。

多目标跟踪中的网络流全局目标学习

Learning of Global Objective for Network Flow in Multi-Object Tracking

The crux of long-term tracking lies in the difficulty of tracking the target
with discontinuous moving caused by out-of-view or occlusion. Existing
long-term tracking methods follow two typical strategies. The first strategy
employs a local tracker to perform smooth tracking and uses another re-detector
to detect the target when the target is lost. While it can exploit the temporal
context like historical appearances and locations of the target, a potential
limitation of such strategy is that the local tracker tends to misidentify a
nearby distractor as the target instead of activating the re-detector when the
real target is out of view. The other long-term tracking strategy tracks the
target in the entire image globally instead of local tracking based on the
previous tracking results. Unfortunately, such global tracking strategy cannot
leverage the temporal context effectively. In this work, we combine the
advantages of both strategies: tracking the target in a global view while
exploiting the temporal context. Specifically, we perform global tracking via
ensemble of local trackers spreading the full image. The smooth moving of the
target can be handled steadily by one local tracker. When the local tracker
accidentally loses the target due to suddenly discontinuous moving, another
local tracker close to the target is then activated and can readily take over
the tracking to locate the target. While the activated local tracker performs
tracking locally by leveraging the temporal context, the ensemble of local
trackers renders our model the global view for tracking. Extensive experiments
on six datasets demonstrate that our method performs favorably against
state-of-the-art algorithms.

本研究结合了全局追踪和利用时间上下文的优点，通过局部追踪器的集成来全局追踪目标。实验表明我们的方法在六个数据集上表现优异。