  Multi-person pose tracking is an important element for many applications and
requires to estimate the human poses of all persons in a video and to track
them over time. The association of poses across frames remains an open research
problem, in particular for online tracking methods, due to motion blur, crowded
scenes and occlusions. To tackle the association challenge, we propose a Gated
Attention Transformer. The core aspect of our model is the gating mechanism
that automatically adapts the impact of appearance embeddings and embeddings
based on temporal pose similarity in the attention layers. In order to
re-identify persons that have been occluded, we incorporate a pose-conditioned
re-identification network that provides initial embeddings and allows to match
persons even if the number of visible joints differ between frames. We further
propose a matching layer based on gated attention for pose-to-track association
and duplicate removal. We evaluate our approach on PoseTrack 2018 and
PoseTrack21.
