AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding
  for Multi-Object Tracking
Vitaliy Kim Gunho Jung Seong-Whan Lee
Many multi-object tracking (MOT) approaches, which employ the Kalman Filter
as a motion predictor, assume constant velocity and Gaussian-distributed
filtering noises. These assumptions render the Kalman Filter-based trackers
effective in linear motion scenarios. However, these linear assumptions serve
as a key limitation when estimating future object locations within scenarios
involving non-linear motion and occlusions. To address this issue, we propose a
motion-based MOT approach with an adaptable motion predictor, called AM-SORT,
which adapts to estimate non-linear uncertainties. AM-SORT is a novel extension
of the SORT-series trackers that supersedes the Kalman Filter with the
transformer architecture as a motion predictor. We introduce a historical
trajectory embedding that empowers the transformer to extract spatio-temporal
features from a sequence of bounding boxes. AM-SORT achieves competitive
performance compared to state-of-the-art trackers on DanceTrack, with 56.3 IDF1
and 55.6 HOTA. We conduct extensive experiments to demonstrate the
effectiveness of our method in predicting non-linear movement under occlusions.