We developed a minimum-cost circulation framework for solving the global data association problem, which plays a key role in the tracking-by-detection paradigm of multi-object tracking (MOT). The global data association problem was extensively studied under the minimum-cost flow framework, which is theoretically attractive as being flexible and globally solvable. However, the high computational burden has been a long-standing obstacle to its wide adoption in practice. While enjoying the same theoretical advantages and maintaining the same optimal solution as the minimum-cost flow framework, our new framework has a better theoretical complexity bound and leads to orders of practical efficiency improvement. This new framework is motivated by the observation that minimum-cost flow only partially models the data association problem and it must be accompanied by an additional and time-consuming searching scheme to determine the optimal object number. By employing a minimum-cost circulation framework, we eliminate the searching step and naturally integrate the number of objects into the optimization problem. By exploring the special property of the associated graph, that is, an overwhelming majority of the vertices are with unit capacity, we designed an implementation of the framework and proved it has the best theoretical computational complexity so far for the global data association problem. We evaluated our method with 40 experiments on five MOT benchmark datasets. Our method was always the most efficient in every single experiment and averagely 53 to 1,192 times faster than the three state-of-the-art methods. When our method served as a sub-module for global data association methods utilizing higher-order constraints, similar running time improvement was attained. We further illustrated through several case studies how the improved computational efficiency enables more sophisticated tracking models and yields better tracking accuracy. We made the source code publicly available on GitHub with both Python and MATLAB interfaces.

中文翻译：

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最低成本流通框架下的高效全球MOT


我们开发了一个最小成本循环框架来解决全局数据关联问题，该框架在多目标跟踪（MOT）的检测跟踪范式中发挥着关键作用。全局数据关联问题在最小成本流框架下得到了广泛的研究，该框架由于灵活且可全局解决而在理论上具有吸引力。然而，高计算负担一直是其在实践中广泛采用的长期障碍。我们的新框架在享有与最小成本流框架相同的理论优势并保持相同的最优解的同时，具有更好的理论复杂度界限，并导致实际效率的提高。这个新框架的动机是观察到最小成本流仅部分模拟数据关联问题，并且必须伴随额外且耗时的搜索方案来确定最佳对象数量。通过采用最小成本循环框架，我们消除了搜索步骤，并自然地将对象数量集成到优化问题中。通过探索关联图的特殊性质，即绝大多数顶点具有单位容量，我们设计了该框架的实现，并证明其对于全局数据关联问题具有迄今为止最好的理论计算复杂度。我们在 5 个 MOT 基准数据集上进行了 40 次实验来评估我们的方法。我们的方法在每个实验中始终是最有效的，平均比三种最先进的方法快 53 至 1,192 倍。当我们的方法用作利用高阶约束的全局数据关联方法的子模块时，获得了类似的运行时间改进。 我们通过几个案例研究进一步说明了计算效率的提高如何实现更复杂的跟踪模型并产生更好的跟踪精度。我们通过 Python 和 MATLAB 接口在 GitHub 上公开提供源代码。