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DeepWiTraffic: Low cost WiFi-based traffic monitoring system using deep learning
- DeepWiTraffic: Low cost WiFi-based traffic monitoring system using deep learning
- Won Myounggyu; Sahu Sayan; Park, Kyung-Joon
- DGIST Authors
- Park, Kyung-Joon
- Issue Date
- 16th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2019, 476-484
- A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS). It is an essential tool for traffic analysis and planning. One of the biggest challenges is, however, the high cost especially in covering the huge rural road network. In this paper, we propose to address the problem by developing a novel TMS called DeepWiTraffic. DeepWiTraffic is a low-cost, portable, and non-intrusive solution that is built only with two WiFi transceivers. It exploits the unique WiFi Channel State Information (CSI) of passing vehicles to perform detection and classification of vehicles. Spatial and temporal correlations of CSI amplitude and phase data are identified and analyzed using a machine learning technique to classify vehicles into five different types: motorcycles, passenger vehicles, SUVs, pickup trucks, and large trucks. A large amount of CSI data and ground-truth video data are collected over a month period from a real-world two-lane rural roadway to validate the effectiveness of DeepWiTraffic. The results validate that DeepWiTraffic is an effective TMS with the average detection accuracy of 99.4% and the average classification accuracy of 91.1% in comparison with state-of-the-art non-intrusive TMSs. © 2019 IEEE.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
CSI(Cyber-Physical Systems Integration) Lab
Cyber-Physical Systems; 무선 센서-액츄에이터 네트워크; 스마트 팩토리
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- Department of Information and Communication EngineeringCSI(Cyber-Physical Systems Integration) Lab2. Conference Papers
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