DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction
CVPR 2024(2024)
Abstract
In Multiple Object Tracking, objects often exhibit non-linear motion of
acceleration and deceleration, with irregular direction changes.
Tacking-by-detection (TBD) with Kalman Filter motion prediction works well in
pedestrian-dominant scenarios but falls 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 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 less 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 63.4 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.
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