Multiple object tracking is an important but challenging computer vision problem. The complex motion of objects makes tracking difficult during long periods of object occlusion, and as a result occlusions frequently cause fragmented tracks with gaps. Previous works use linear interpolation to fill in such gaps, a technique which is only able to model simple motion. As a result, tracked bounding box locations can be quite poor in these situations. In this paper, we propose a 1D CNN based solution to filling gaps which models complex motion in a data-driven way. Our proposed solution uses only bounding box coordinates as input, and as such does not incur the computational cost of processing image features directly. We show that our model significantly outperforms linear interpolation on dynamic sports datasets in terms of mean intersection over union between predicted and ground truth bounding boxes.

多对象跟踪是一个重要但具有挑战性的计算机视觉问题。物体的复杂运动使得在长时间的物体遮挡过程中很难进行跟踪，因此，遮挡经常会导致带有间隙的碎片轨道。以前的工作使用线性插值法来填补这种空白，该技术只能对简单的运动建模。结果，在这些情况下，跟踪的边界框位置可能会很差。在本文中，我们提出了一种基于一维CNN的解决方案来填补空白，该解决方案以数据驱动的方式对复杂运动进行建模。我们提出的解决方案仅使用边界框坐标作为输入，因此不会直接产生处理图像特征的计算成本。