3D Multi-Object Tracking Driven by Multi-Level Association and Intelligent Filtering

3D multi-object tracking has been extensively applied in areas such as autonomous driving, uncrewed aerial vehicles, and robots. However, existing 3D multi-object tracking methods still face challenges including inaccurately fitting the true motion of objects, insufficient utilization of trajectory information, and trajectory drift. To address these issues, we propose a 3D multi-object tracking framework driven by multi-level association and intelligent filtering. We design an adaptive prediction module for state estimation that reduces noise and error in prediction information through estimation and smoothing processes, thereby enhancing the stability and accuracy of state prediction. Subsequently, a multi-level trajectory integration-guided association strategy is introduced. This strategy integrates detection results, trajectory data, and potential real-world states of the objects. It minimizes incorrect associations and identity switches, thus achieving more accurate and robust data association. Finally, we propose a quality-aware intelligent filtering module for trajectory correction. This module assesses the quality of all matched detection-trajectory pairs and applies regression correction to low-quality drift detections, effectively reducing trajectory fragmentization and identity switches. On the KITTI test dataset, our method achieves 80.64% HOTA and 53.68% HOTA for the car and pedestrian categories, respectively, while reducing identity switches to 50 and 97. These results demonstrate that, compared with existing methods, our method delivers superior tracking performance and exhibits stronger robustness in handling challenges such as large inter-frame displacements, long-term occlusions, and identity switches.