  Referring multi-object tracking (RMOT) aims to track multiple objects based
on input textual descriptions. Previous works realize it by simply integrating
an extra textual module into the multi-object tracker. However, they typically
need to retrain the entire framework and have difficulties in optimization. In
this work, we propose an insertable Knowledge Unification Network, termed iKUN,
to enable communication with off-the-shelf trackers in a plug-and-play manner.
Concretely, a knowledge unification module (KUM) is designed to adaptively
extract visual features based on textual guidance. Meanwhile, to improve the
localization accuracy, we present a neural version of Kalman filter (NKF) to
dynamically adjust process noise and observation noise based on the current
motion status. Moreover, to address the problem of open-set long-tail
distribution of textual descriptions, a test-time similarity calibration method
is proposed to refine the confidence score with pseudo frequency. Extensive
experiments on Refer-KITTI dataset verify the effectiveness of our framework.
Finally, to speed up the development of RMOT, we also contribute a more
challenging dataset, Refer-Dance, by extending public DanceTrack dataset with
motion and dressing descriptions. The codes and dataset are available at
https://github.com/dyhBUPT/iKUN.
