DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction

• URL: http://arxiv.org/abs/2403.02075v2
• Date: Wed, 20 Mar 2024 14:52:27 GMT
• Title: DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction
• Authors: Weiyi Lv, Yuhang Huang, Ning Zhang, Ruei-Sung Lin, Mei Han, Dan Zeng,
• Abstract summary: We propose a real-time diffusion-based MOT approach named DiffMOT to tackle the complex non-linear motion. 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.
• Score: 15.542306419065945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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