TrackFormer: Multi-Object Tracking with Transformers

Laura Leal-Taixé, Tim Meinhardt, Alexander Kirillov, Christoph Feichtenhofer

Abstract:

The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization,
identity, and spatio-temporal trajectories. We formulate
this task as a frame-to-frame set prediction problem and
introduce TrackFormer, an end-to-end trainable MOT approach based on an encoder-decoder Transformer architecture. Our model achieves data association between frames
via attention by evolving a set of track predictions through
a video sequence. The Transformer decoder initializes new
tracks from static object queries and autoregressively follows existing tracks in space and time with the conceptually new and identity preserving track queries. Both
query types benefit from self- and encoder-decoder attention on global frame-level features, thereby omitting any additional graph optimization or modeling of motion and/or
appearance. TrackFormer introduces a new tracking-byattention paradigm and while simple in its design is able to
achieve state-of-the-art performance on the task of multiobject tracking (MOT17 and MOT20) and segmentation
(MOTS20). The code is available at https://github.
com/timmeinhardt/trackformer.