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<p>Wi-Fi sensing is an innovative technology that enables numerous human-related applications. Among these, Wi-Fi based person re-identification (Re-ID) is an emerging research topic aiming to address well-known challenges related to traditional vision-based methods, such as occlusions or illumination changes. This approach can serve as either an alternative or a supplementary solution to those conventional techniques. However, public datasets and benchmarks for Wi-Fi based person Re-ID are still missing, posing constraints on future investigations. Towards filling this gap, this paper presents Wi-PER81, a pioneering dataset comprising measurements of 162,000 wireless packets captured at two different times, associated with 81 distinct identities. Furthermore, it introduces a baseline Siamese neural network architecture used to analyze person-related signal magnitude heatmaps and the results of a comparative study against well-known neural network models, serving as backbones in the proposed approach, that provides a comprehensive benchmark for person Re-ID using radio-based visual features.</p>

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A Benchmark Dataset for Radio Signal Image-based Person Re-Identification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月30日

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  • Marco Cascio,
  • Luigi Cinque,
  • Damiano Distante,
  • Gian Luca Foresti,
  • Alessio Fagioli

摘要

Wi-Fi sensing is an innovative technology that enables numerous human-related applications. Among these, Wi-Fi based person re-identification (Re-ID) is an emerging research topic aiming to address well-known challenges related to traditional vision-based methods, such as occlusions or illumination changes. This approach can serve as either an alternative or a supplementary solution to those conventional techniques. However, public datasets and benchmarks for Wi-Fi based person Re-ID are still missing, posing constraints on future investigations. Towards filling this gap, this paper presents Wi-PER81, a pioneering dataset comprising measurements of 162,000 wireless packets captured at two different times, associated with 81 distinct identities. Furthermore, it introduces a baseline Siamese neural network architecture used to analyze person-related signal magnitude heatmaps and the results of a comparative study against well-known neural network models, serving as backbones in the proposed approach, that provides a comprehensive benchmark for person Re-ID using radio-based visual features.