Vehicle classification using raspberry pi: a guide to capturing wifi csi data
dc.contributor.author | Schmidt, T | |
dc.contributor.author | Ambegoda, T | |
dc.contributor.author | Gunasekera, K | |
dc.contributor.editor | Abeysooriya, R | |
dc.contributor.editor | Adikariwattage, V | |
dc.contributor.editor | Hemachandra, K | |
dc.date.accessioned | 2024-03-01T04:36:18Z | |
dc.date.available | 2024-03-01T04:36:18Z | |
dc.date.issued | 2023-12-09 | |
dc.description.abstract | Traffic monitoring Systems are an essential data collection tool in traffic analysis and transport planning. In this paper, we aim to identify whether the popular low-cost single board computer Raspberry Pi 3B+ can be used as an alternative to existing solutions which require bulky and expensive setups to effectively capture WiFi CSI data for vehicle classification. We also look into the problems faced in this implementation and their solutions.We propose a data processing pipeline for this approach to aid in creating an annotated dataset for the classification of vehicle types. The results show that the proposed system can successfully capture WiFi CSI data for vehicle classification. | en_US |
dc.identifier.citation | T. Schmidt, T. Ambegoda and K. Gunasekera, "Vehicle Classification Using Raspberry Pi: A Guide to Capturing WiFi CSI Data," 2023 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2023, pp. 702-707, doi: 10.1109/MERCon60487.2023.10355444. | en_US |
dc.identifier.conference | Moratuwa Engineering Research Conference 2023 | en_US |
dc.identifier.department | Engineering Research Unit, University of Moratuwa | en_US |
dc.identifier.email | tharidhu.22@cse.mrt.ac.lk | en_US |
dc.identifier.email | thanuja@cse.mrt.ac.lk | en_US |
dc.identifier.email | kutila@cse.mrt.ac.lk | en_US |
dc.identifier.faculty | Engineering | en_US |
dc.identifier.pgnos | pp. 702-707 | en_US |
dc.identifier.place | Katubedda | en_US |
dc.identifier.proceeding | Proceedings of Moratuwa Engineering Research Conference 2023 | en_US |
dc.identifier.uri | http://dl.lib.uom.lk/handle/123/22237 | |
dc.identifier.year | 2023 | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.uri | https://ieeexplore.ieee.org/document/10355444 | en_US |
dc.subject | Channel state information (CSI) | en_US |
dc.subject | Raspberry Pi | en_US |
dc.subject | Nexmon | en_US |
dc.subject | Vehicle classification | en_US |
dc.title | Vehicle classification using raspberry pi: a guide to capturing wifi csi data | en_US |
dc.type | Conference-Full-text | en_US |