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Updated: Apr 15, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Published on: May 1, 2018

Robust 3D Multi-Object Tracking via 4D mmWave Radar-Camera Fusion and Disparity-Domain Depth Recovery.

Yunfei Xie^1,2, Xiaohui Li², Dingheng Wang¹

¹Northwest Institute of Mechanical and Electrical Engineering, Xianyang 712099, China.

Sensors (Basel, Switzerland)

|April 14, 2026

Summary

This summary is machine-generated.

This study introduces a radar-camera fusion framework for 3D multi-object tracking, enhancing perception in adverse conditions. The method achieves high accuracy by effectively fusing sensor data without complex annotations.

Keywords:

4D millimeter-wave radar disparity-domain recalibration monocular depth estimation multi-object tracking sensor fusion

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Last Updated: Apr 15, 2026

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A Protocol for Real-time 3D Single Particle Tracking

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Area of Science:

Robotics and Autonomous Systems
Computer Vision
Sensor Fusion

Background:

4D millimeter-wave radar offers robust, high-precision ranging but has sparse point clouds and elevation noise.
Monocular cameras provide rich semantic data but suffer from scale ambiguity.
Exploiting complementary sensor strengths is crucial for advanced environmental perception.

Purpose of the Study:

To develop a radar-camera fusion 3D multi-object tracking framework.
To enable robust perception in complex dynamic scenarios without requiring 3D annotated data.
To improve the accuracy and reliability of object tracking by integrating diverse sensor modalities.

Main Methods:

Enhanced radar signal processing with adaptive angle compression and IMU velocity compensation.
Improved DBSCAN clustering for high-quality radar detections.
Disparity-domain metric depth recovery using radar points as anchors and Kalman filtering.
Hierarchical fusion strategy at detection and tracking levels for cross-modal state association.

Main Results:

The proposed framework achieved an overall MOTA of 77.93% on a self-collected dataset.
Significantly outperformed single-modality baselines and other comparison methods by 11-31 percentage points.
Demonstrated effective suppression of radar measurement noise and robust depth recovery.

Conclusions:

The radar-camera fusion approach provides an effective solution for low-cost, robust environment perception.
The method successfully addresses limitations of individual sensors for 3D multi-object tracking.
This work advances cross-modal sensor fusion for autonomous systems in challenging conditions.