WiCross: Indoor Human Zone-Crossing Detection Using Commodity WiFi Devices
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910925789855744 |
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| author | Shi, Weiyan Wang, Xuanzhi Niu, Kai Wang, Leye Zhang, Daqing |
| author_facet | Shi, Weiyan Wang, Xuanzhi Niu, Kai Wang, Leye Zhang, Daqing |
| contents | Detecting whether a target crosses the given zone (e.g., a door) can enable various practical applications in smart homes, including intelligent security and people counting. The traditional infrared-based approach only covers a line and can be easily cracked. In contrast, reusing the ubiquitous WiFi devices deployed in homes has the potential to cover a larger area of interest as WiFi signals are scattered throughout the entire space. By detecting the walking direction (i.e., approaching and moving away) with WiFi signal strength change, existing work can identify the behavior of crossing between WiFi transceiver pair. However, this method mistakenly classifies the turn-back behavior as crossing behavior, resulting in a high false alarm rate. In this paper, we propose WiCross, which can accurately distinguish the turn-back behavior with the phase statistics pattern of WiFi signals and thus robustly identify whether the target crosses the area between the WiFi transceiver pair. We implement WiCross with commercial WiFi devices and extensive experiments demonstrate that WiCross can achieve an accuracy higher than 95\% with a false alarm rate of less than 5%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WiCross: Indoor Human Zone-Crossing Detection Using Commodity WiFi Devices Shi, Weiyan Wang, Xuanzhi Niu, Kai Wang, Leye Zhang, Daqing Human-Computer Interaction Detecting whether a target crosses the given zone (e.g., a door) can enable various practical applications in smart homes, including intelligent security and people counting. The traditional infrared-based approach only covers a line and can be easily cracked. In contrast, reusing the ubiquitous WiFi devices deployed in homes has the potential to cover a larger area of interest as WiFi signals are scattered throughout the entire space. By detecting the walking direction (i.e., approaching and moving away) with WiFi signal strength change, existing work can identify the behavior of crossing between WiFi transceiver pair. However, this method mistakenly classifies the turn-back behavior as crossing behavior, resulting in a high false alarm rate. In this paper, we propose WiCross, which can accurately distinguish the turn-back behavior with the phase statistics pattern of WiFi signals and thus robustly identify whether the target crosses the area between the WiFi transceiver pair. We implement WiCross with commercial WiFi devices and extensive experiments demonstrate that WiCross can achieve an accuracy higher than 95\% with a false alarm rate of less than 5%. |
| title | WiCross: Indoor Human Zone-Crossing Detection Using Commodity WiFi Devices |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.20331 |