Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data
Accurate detection of locations of indoor high-density crowds is crucial for early warning and emergency rescue during indoor safety accidents. The spatial structure of indoor environments is more complicated than outdoor environments. The locations of indoor high-density crowds are more likely to b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/18/5078 |
| _version_ | 1827706748782247936 |
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| author | Peixiao Wang Fei Gao Yuhui Zhao Ming Li Xinyan Zhu |
| author_facet | Peixiao Wang Fei Gao Yuhui Zhao Ming Li Xinyan Zhu |
| author_sort | Peixiao Wang |
| collection | DOAJ |
| description | Accurate detection of locations of indoor high-density crowds is crucial for early warning and emergency rescue during indoor safety accidents. The spatial structure of indoor environments is more complicated than outdoor environments. The locations of indoor high-density crowds are more likely to be the sites of security accidents. Existing detection methods for high-density crowd locations mostly focus on outdoor environments, and relatively few detection methods exist for indoor environments. This study proposes a novel detection framework for high-density indoor crowd locations termed IndoorSRC (Simplification–Reconstruction–Cluster). In this paper, a novel indoor spatiotemporal clustering algorithm called Indoor-STAGNES is proposed to detect the indoor trajectory stay points to simplify indoor movement trajectory. Then, we propose use of a Kalman filter algorithm to reconstruct the indoor trajectory and properly align and resample the data. Finally, an indoor spatiotemporal density clustering algorithm called Indoor-STOPTICS is proposed to detect the locations of high-density crowds in the indoor environment from the reconstructed trajectory. Extensive experiments were conducted using indoor Wi-Fi positioning datasets collected from a shopping mall. The results show that the IndoorSRC framework evidently outperforms the existing baseline method in terms of detection performance. |
| first_indexed | 2024-03-10T16:30:49Z |
| format | Article |
| id | doaj.art-8a07f28102524d41ae3bc243c3bc798e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:30:49Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8a07f28102524d41ae3bc243c3bc798e2023-11-20T12:50:07ZengMDPI AGSensors1424-82202020-09-012018507810.3390/s20185078Detection of Indoor High-Density Crowds via Wi-Fi Tracking DataPeixiao Wang0Fei Gao1Yuhui Zhao2Ming Li3Xinyan Zhu4State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, ChinaInstitute of Space Science and Technology, Nanchang University, Nanchang 330031, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, ChinaAccurate detection of locations of indoor high-density crowds is crucial for early warning and emergency rescue during indoor safety accidents. The spatial structure of indoor environments is more complicated than outdoor environments. The locations of indoor high-density crowds are more likely to be the sites of security accidents. Existing detection methods for high-density crowd locations mostly focus on outdoor environments, and relatively few detection methods exist for indoor environments. This study proposes a novel detection framework for high-density indoor crowd locations termed IndoorSRC (Simplification–Reconstruction–Cluster). In this paper, a novel indoor spatiotemporal clustering algorithm called Indoor-STAGNES is proposed to detect the indoor trajectory stay points to simplify indoor movement trajectory. Then, we propose use of a Kalman filter algorithm to reconstruct the indoor trajectory and properly align and resample the data. Finally, an indoor spatiotemporal density clustering algorithm called Indoor-STOPTICS is proposed to detect the locations of high-density crowds in the indoor environment from the reconstructed trajectory. Extensive experiments were conducted using indoor Wi-Fi positioning datasets collected from a shopping mall. The results show that the IndoorSRC framework evidently outperforms the existing baseline method in terms of detection performance.https://www.mdpi.com/1424-8220/20/18/5078high-density crowd location detectionindoor trajectoryIndoor-STAGNESIndoor-STOPTICS |
| spellingShingle | Peixiao Wang Fei Gao Yuhui Zhao Ming Li Xinyan Zhu Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data Sensors high-density crowd location detection indoor trajectory Indoor-STAGNES Indoor-STOPTICS |
| title | Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data |
| title_full | Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data |
| title_fullStr | Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data |
| title_full_unstemmed | Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data |
| title_short | Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data |
| title_sort | detection of indoor high density crowds via wi fi tracking data |
| topic | high-density crowd location detection indoor trajectory Indoor-STAGNES Indoor-STOPTICS |
| url | https://www.mdpi.com/1424-8220/20/18/5078 |
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