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...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 20; H. 18; S. 5078
Hauptverfasser: Wang, Peixiao, Gao, Fei, Zhao, Yuhui, Li, Ming, Zhu, Xinyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 07.09.2020
MDPI
Schlagworte:
Accuracy
Algorithms
Clustering
high-density crowd location detection
indoor trajectory
Indoor-STAGNES
Indoor-STOPTICS
Traffic congestion
Traffic control
Traffic flow
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20185078