  Multi-object tracking (MOT) requires detecting and associating objects
through frames. Unlike tracking via detected bounding boxes or tracking objects
as points, we propose tracking objects as pixel-wise distributions. We
instantiate this idea on a transformer-based architecture, P3AFormer, with
pixel-wise propagation, prediction, and association. P3AFormer propagates
pixel-wise features guided by flow information to pass messages between frames.
Furthermore, P3AFormer adopts a meta-architecture to produce multi-scale object
feature maps. During inference, a pixel-wise association procedure is proposed
to recover object connections through frames based on the pixel-wise
prediction. P3AFormer yields 81.2\% in terms of MOTA on the MOT17 benchmark --
the first among all transformer networks to reach 80\% MOTA in literature.
P3AFormer also outperforms state-of-the-arts on the MOT20 and KITTI benchmarks.
