  Significant progress has been achieved in multi-object tracking (MOT) through
the evolution of detection and re-identification (ReID) techniques. Despite
these advancements, accurately tracking objects in scenarios with homogeneous
appearance and heterogeneous motion remains a challenge. This challenge arises
from two main factors: the insufficient discriminability of ReID features and
the predominant utilization of linear motion models in MOT. In this context, we
introduce a novel motion-based tracker, MotionTrack, centered around a
learnable motion predictor that relies solely on object trajectory information.
This predictor comprehensively integrates two levels of granularity in motion
features to enhance the modeling of temporal dynamics and facilitate precise
future motion prediction for individual objects. Specifically, the proposed
approach adopts a self-attention mechanism to capture token-level information
and a Dynamic MLP layer to model channel-level features. MotionTrack is a
simple, online tracking approach. Our experimental results demonstrate that
MotionTrack yields state-of-the-art performance on datasets such as Dancetrack
and SportsMOT, characterized by highly complex object motion.
