  In Multiple Object Tracking, objects often exhibit non-linear motion of
acceleration and deceleration, with irregular direction changes.
Tacking-by-detection (TBD) trackers with Kalman Filter motion prediction work
well in pedestrian-dominant scenarios but fall short in complex situations when
multiple objects perform non-linear and diverse motion simultaneously. To
tackle the complex non-linear motion, we propose a real-time diffusion-based
MOT approach named DiffMOT. Specifically, for the motion predictor component,
we propose a novel Decoupled Diffusion-based Motion Predictor (D$^2$MP). It
models the entire distribution of various motion presented by the data as a
whole. It also predicts an individual object's motion conditioning on an
individual's historical motion information. Furthermore, it optimizes the
diffusion process with much fewer sampling steps. As a MOT tracker, the DiffMOT
is real-time at 22.7FPS, and also outperforms the state-of-the-art on
DanceTrack and SportsMOT datasets with $62.3\%$ and $76.2\%$ in HOTA metrics,
respectively. To the best of our knowledge, DiffMOT is the first to introduce a
diffusion probabilistic model into the MOT to tackle non-linear motion
prediction.
