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This paper proposes an efficient, real-time method for recognizing and tracking multiple objects using sparse point-cloud sequences generated by a low-cost mmWave radar. The system employs the DBSCAN algorithm to cluster the radar’s point cloud data, capturing objects across multiple frames within defined windows. A moving average filter is applied to mitigate measurement errors in the radar data. During the tracking phase, a Kalman filter predicts object positions, while the Hungarian algorithm ensures the correct assignment of detections to specific tracks. The proposed method is evaluated through five experiments, where people move within the radar’s field of view. These experiments involve overlapping people, making the tracking algorithm particularly challenging. The Multi-Object Tracking Accuracy (MOTA) metric is used to assess the results, achieving a 90.10% accuracy rate, which underscores the method’s potential for real-time multi-object tracking using mmWave radar. Videos of the experiments can be accessed via the following link: https://github.com/nabihandres/RADAR_tracking_tests.git

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Real-Time Multi-object Tracking and Identification Using Sparse Point-Cloud Data from Low-Cost mmWave Radar

  • 会议论文

  • 发表日期：

    2025年

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  • Nabih Pico,
  • Maykoll Vanegas,
  • Eugene Auh,
  • Hong-ryul Jung,
  • Altair Coutinho,
  • Elvia Montero,
  • Hyungpil Moon

摘要

This paper proposes an efficient, real-time method for recognizing and tracking multiple objects using sparse point-cloud sequences generated by a low-cost mmWave radar. The system employs the DBSCAN algorithm to cluster the radar’s point cloud data, capturing objects across multiple frames within defined windows. A moving average filter is applied to mitigate measurement errors in the radar data. During the tracking phase, a Kalman filter predicts object positions, while the Hungarian algorithm ensures the correct assignment of detections to specific tracks. The proposed method is evaluated through five experiments, where people move within the radar’s field of view. These experiments involve overlapping people, making the tracking algorithm particularly challenging. The Multi-Object Tracking Accuracy (MOTA) metric is used to assess the results, achieving a 90.10% accuracy rate, which underscores the method’s potential for real-time multi-object tracking using mmWave radar. Videos of the experiments can be accessed via the following link: https://github.com/nabihandres/RADAR_tracking_tests.git