IEEE Trans Image Process. 2017 Oct;26(10):4765-4776. doi: 10.1109/TIP.2017.2723239. Epub 2017 Jul 4.
Minimum-cost flow algorithms have recently achieved state-of-the-art results in multi-object tracking. However, they rely on the whole image sequence as input. When deployed in real-time applications or in distributed settings, these algorithms first operate on short batches of frames and then stitch the results into full trajectories. This decoupled strategy is prone to errors because the batch-based tracking errors may propagate to the final trajectories and cannot be corrected by other batches. In this paper, we propose a greedy batch-based minimum-cost flow approach for tracking multiple objects. Unlike existing approaches that conduct batch-based tracking and stitching sequentially, we optimize consecutive batches jointly so that the tracking results on one batch may benefit the results on the other. Specifically, we apply a generalized minimum-cost flows (MCF) algorithm on each batch and generate a set of conflicting trajectories. These trajectories comprise the ones with high probabilities, but also those with low probabilities potentially missed by detectors and trackers. We then apply the generalized MCF again to obtain the optimal matching between trajectories from consecutive batches. Our proposed approach is simple, effective, and does not require training. We demonstrate the power of our approach on data sets of different scenarios.
最近，最小成本流算法在多目标跟踪方面取得了最先进的成果。然而，它们依赖于整个图像序列作为输入。当部署在实时应用程序或分布式环境中时，这些算法首先对短批次的帧进行操作，然后将结果拼接成完整的轨迹。这种解耦策略容易出错，因为基于批处理的跟踪错误可能会传播到最终轨迹，而其他批次无法纠正这些错误。在本文中，我们提出了一种用于跟踪多个对象的贪婪基于批处理的最小成本流方法。与现有的按顺序进行基于批处理的跟踪和拼接的方法不同，我们联合优化连续的批处理，以便一个批处理上的跟踪结果可以受益于另一个批处理上的结果。具体来说，我们在每个批处理上应用广义最小成本流（MCF）算法，并生成一组冲突轨迹。这些轨迹包括高概率的轨迹，但也包括那些可能被检测器和跟踪器错过的低概率轨迹。然后，我们再次应用广义 MCF 来获得连续批次之间轨迹的最佳匹配。我们提出的方法简单、有效，且不需要训练。我们在不同场景的数据集上展示了我们方法的强大之处。
