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Multi-user tracking in indoor environments is a challenging but essential task. Existing methods often struggle with challenges such as sensor noise, overlapping users, and environmental dynamics. This paper proposes a novel approach using the Probability Hypothesis Density (PHD) filter within the framework of Random Finite Sets (RFS) for enhanced tracking performance. Experimental results on a combined camera-radar dataset demonstrate the effectiveness of the proposed method, showcasing promising tracking accuracy and reliability in dynamic indoor settings.

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Multimodal Multiuser Tracking in Indoor Environment Using Probability Hypothesis Density (PHD) Filter

  • 会议论文

  • 发表日期：

    2026年

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  • Trung-Kien Dao,
  • Come Perrin,
  • Dinh-Van Nguyen,
  • Quang-Vinh Khuat,
  • Viet-Tung Nguyen,
  • Van-Phuong Ha

摘要

Multi-user tracking in indoor environments is a challenging but essential task. Existing methods often struggle with challenges such as sensor noise, overlapping users, and environmental dynamics. This paper proposes a novel approach using the Probability Hypothesis Density (PHD) filter within the framework of Random Finite Sets (RFS) for enhanced tracking performance. Experimental results on a combined camera-radar dataset demonstrate the effectiveness of the proposed method, showcasing promising tracking accuracy and reliability in dynamic indoor settings.