Multi-object tracking (MOT) or global data association problem is commonly approached as a minimum-cost-flow or minimum-cost-circulation problem on a graph. While there have been numerous studies aimed at enhancing algorithm efficiency, most of them focus on the batch problem, where all the data must be available simultaneously to construct a static graph. However, with the growing number of applications that generate streaming data, an efficient online algorithm is required to handle the streaming nature of the input. In this paper, we present an online extension of the well-known negative cycle canceling algorithm for solving the multi-object tracking problem with streaming fragmented data. We provide a proof of correctness for the proposed algorithm and demonstrate its efficiency through numerical experiments.

翻译：多目标跟踪（MOT）或全局数据关联问题通常被建模为图上的最小费用流或最小费用环流问题。尽管已有大量研究致力于提升算法效率，但多数方法聚焦于批处理问题，即需要所有数据同时可用以构建静态图。然而，随着产生流式数据的应用日益增多，需开发高效在线算法以应对输入的流式特性。本文针对流式碎片化数据的多目标跟踪问题，提出了一种经典负环消去算法的在线扩展版本。我们给出了所提算法的正确性证明，并通过数值实验验证了其效率。