  In this paper, we present an algorithm to tackle a video panoptic
segmentation problem, a newly emerging area of research. The video panoptic
segmentation is a task that unifies the typical task of panoptic segmentation
and multi-object tracking. In other words, it requires generating the instance
tracking IDs along with panoptic segmentation results across video sequences.
Our proposed video panoptic segmentation algorithm uses the transformer and it
can be trained in end-to-end with an input of multiple video frames. We test
our method on the STEP dataset and report its performance with recently
proposed STQ metric. The method archived 57.81\% on the KITTI-STEP dataset and
31.8\% on the MOTChallenge-STEP dataset.
