HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking

2025-01-02

AI Summary

Existing ADAS multi-object tracking (MOT) methods suffer from limited adaptability and accuracy due to hand-crafted, fixed prediction rules. To address this, we propose a 3D vehicle MOT framework that unifies data-driven Kalman filtering with the detection-tracking paradigm. Our approach is the first to enable end-to-end learning of core Kalman filter parameters—namely, the state transition residual and Kalman gain—thereby preserving physical interpretability while enhancing cross-scenario generalization and eliminating reliance on manual modeling. Leveraging explicit 3D object modeling, our method achieves 82.08% HOTA on the KITTI benchmark, surpassing prior state-of-the-art methods. Moreover, it operates at 112 FPS, demonstrating both high accuracy and real-time capability—critical for safety-critical ADAS applications.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Tracking, profiling, and countermeasures against them

Abstract

The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined motion models and assumptions about system noise distributions. Although computationally efficient, they often lack adaptability to varying traffic scenarios and require extensive manual design and parameter tuning. To address these issues, we propose a novel 3D multi-object tracking approach for vehicles, HybridTrack, which integrates a data-driven Kalman Filter (KF) within a tracking-by-detection paradigm. In particular, it learns the transition residual and Kalman gain directly from data, which eliminates the need for manual motion and stochastic parameter modeling. Validated on the real-world KITTI dataset, HybridTrack achieves 82.08% HOTA accuracy, significantly outperforming state-of-the-art methods. We also evaluate our method under different configurations, achieving the fastest processing speed of 112 FPS. Consequently, HybridTrack eliminates the dependency on scene-specific designs while improving performance and maintaining real-time efficiency. The code will be publicly available at the time of publishing: https://github.com/leandro-svg/HybridTrack.git.

Problem

• Multi-Object Tracking
• Advanced Driver Assistance Systems
• Predictive Algorithms

Innovation

• HybridTrack
• Multi-object Tracking
• ADAS Integration