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DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction (2403.02075v2)

Published 4 Mar 2024 in cs.CV

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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Citations (7)
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Summary

  • The paper presents a novel diffusion-based MOT framework with a decoupled diffusion motion predictor that models complex non-linear trajectories.
  • It outperforms traditional methods by achieving 63.4% HOTA on DanceTrack and 76.2% on SportsMOT, indicating significant accuracy improvements.
  • The method employs a one-step sampling process, attaining an efficient 22.7 FPS, which makes it viable for real-time tracking applications.

Analysis of DiffMOT: A Real-time Diffusion-based Multiple Object Tracker

The paper titled "DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction" introduces a novel approach within the domain of multiple object tracking (MOT), particularly focusing on complex non-linear motion patterns. This approach leverages diffusion probabilistic models to enhance tracking performance in scenarios characterized by dynamic and non-linear object movements, such as those found in DanceTrack and SportsMOT datasets.

Methodology Overview

The core contribution of this paper is the introduction of DiffMOT, a real-time MOT framework incorporating a Decoupled Diffusion-based Motion Predictor (D2^2MP). Unlike traditional techniques relying heavily on linear models like the Kalman Filter, DiffMOT adeptly manages non-linear motion using a probabilistic diffusion model. The D2^2MP component of this framework models the distribution of various motion patterns and predicts an individual object's motion based on historical movement, substantially improving tracking accuracy in non-linear contexts.

One of the notable features of D2^2MP is its optimization of the diffusion process. Traditional diffusion models often require extensive sampling steps, which significantly impact computational efficiency. By employing a one-step sampling process based on decoupled diffusion theory, D2^2MP achieves high efficiency without sacrificing performance.

Empirical Results

The paper reports superior performance of DiffMOT over existing state-of-the-art methods on highly dynamic datasets. For instance, it achieves a 63.4% HOTA score on DanceTrack and 76.2% on SportsMOT, outperforming competitors by a margin of about 2.1% in HOTA. Moreover, DiffMOT operates at a practical 22.7 FPS, demonstrating the feasibility of real-time applications.

In addition to performance metrics, the paper discusses the architectural nuance of embedding motion prediction within a diffusion-based framework, showing its efficacy in avoiding common pitfalls like ID swaps during tracking non-linear moving objects.

Implications and Future Directions

The incorporation of a diffusion-based model in tracking non-linear motion highlights a promising direction for future research in MOT. Traditional methods struggle with the unpredictable dynamics of real-world objects; thus, diffusion-based models could offer a resilient alternative.

DiffMOT's success could pave the way for more nuanced applications in areas such as autonomous driving and sports analysis, where anticipating object trajectories accurately under non-linear motion is crucial.

However, future research directions may focus on addressing some limitations, such as improving long-term tracking capability and managing occlusions. Enhancement in computational efficiency, while ensuring scalability, remains a pertinent challenge. Exploring the synergistic application of deep learning techniques alongside diffusion models could offer hybrid solutions that balance accuracy and performance in diverse motion scenarios.

Conclusion

The paper presents a significant advancement in multiple object tracking through the introduction of a diffusion-based model adept at handling complex non-linear trajectories in real-time. The DiffMOT framework's robust performance on challenging datasets sets a new benchmark and opens avenues for integrating probabilistic approaches in computer vision tasks requiring high adaptability and precision. As AI technologies evolve, such innovative approaches will be critical in achieving sophisticated real-world applications across various dynamic environments.

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