In this paper, we propose a generic boosting framework for multiple object tracking (MOT). Unlike other works tracking objects from zero, our framework uses their results (tracklets) and makes further optimizations. The motivation of us derives from the observation that most modern MOT trackers have been acceptable performance and can yield relatively reliable tracklets; accordingly, we straight focus on the tracklet-level re-identification, which is the most challenging issue in this case. To achieve that goal, we simultaneously utilize the techniques of single object tracking, tracking fragment (tracklets) and re-identification mechanism through casting them into a multi-label energy optimization and then innovatively solving it using the \(\alpha -\)expansion with label costs algorithm. All these techniques inspire recent MOT a lot to mitigate the occlusion problem, but to our knowledge, by far few works explore to reasonably combine them all like us. Furthermore, we introduce a spatial attention to improve the appearance model and a hierarchical clustering as post-process to progressively improve the tracking consistency. Finally, testing results on the most used benchmarks demonstrate the significant effectiveness and generality of our framework, and the importance of each contribution is also verified through ablative studies.

在本文中，我们提出了一个用于多目标跟踪（MOT）的通用提升框架。与其他从零开始跟踪对象的工作不同，我们的框架使用它们的结果（tracklets）并进行进一步优化。我们的动机源于观察到大多数现代 MOT 跟踪器的性能都可以接受，并且可以产生相对可靠的跟踪器；因此，我们直接关注轨迹级别的重新识别，这是本例中最具挑战性的问题。为了实现这一目标，我们同时利用了单个对象跟踪、跟踪片段（tracklets）和重新识别机制的技术，通过将它们转换为多标签能量优化，然后使用\(\alpha -\)用标签成本算法扩展。所有这些技术都激发了最近的 MOT 以减轻遮挡问题，但据我们所知，到目前为止，很少有工作像我们一样探索将它们合理地组合在一起。此外，我们引入了空间注意力来改进外观模型和层次聚类作为后期处理，以逐步提高跟踪一致性。最后，在最常用的基准测试上的测试结果证明了我们框架的显着有效性和通用性，并且每个贡献的重要性也通过 ablative 研究得到了验证。