Lan Long, Wang Xinchao, Tao Dacheng, Huang Thomas S
IEEE Trans Image Process. 2018 Sep;27(9):4585-4597. doi: 10.1109/TIP.2018.2843129. Epub 2018 Jun 1.
In this paper, we propose to exploit the interactions between non-associable tracklets to facilitate multi-object tracking. We introduce two types of tracklet interactions, close interaction and distant interaction. The close interaction imposes physical constraints between two temporally overlapping tracklets and more importantly, allows us to learn local classifiers to distinguish targets that are close to each other in the spatiotemporal domain. The distant interaction, on the other hand, accounts for the higher-order motion and appearance consistency between two temporally isolated tracklets. Our approach is modeled as a binary labeling problem and solved using the efficient Quadratic Pseudo-Boolean Optimization (QPBO). It yields promising tracking performance on the challenging PETS09 and MOT16 dataset. Our code will be made publicly available upon the acceptance of the manuscript.
在本文中，我们提议利用不可关联的小轨迹之间的相互作用来促进多目标跟踪。我们引入了两种类型的小轨迹相互作用，即近距离相互作用和远距离相互作用。近距离相互作用在两个时间上重叠的小轨迹之间施加物理约束，更重要的是，使我们能够学习局部分类器，以区分在时空域中彼此接近的目标。另一方面，远距离相互作用考虑了两个时间上孤立的小轨迹之间的高阶运动和外观一致性。我们的方法被建模为一个二元标记问题，并使用高效的二次伪布尔优化（QPBO）来解决。它在具有挑战性的PETS09和MOT16数据集上产生了有前景的跟踪性能。一旦稿件被接受，我们的代码将公开提供。
