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Wireless-Sensing-Based Human-Vehicle Classification Method via Deep Learning: Analysis and Implementation
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DOI：10.1109/JIOT.2024.3352561.png)
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摘要
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Wireless-sensing-based human-vehicle classification (WHVC) offer cost-effective advantages and enhance the detection efficiency of traffic parameters in intelligent transportation systems (ITSs). Existing WHVC methods primarily utilize channel state information (CSI) or received signal strength (RSS) features extracted from the surrounding wireless signals. Although CSI data provides more detailed and accurate channel information compared to RSS data, extracting and processing CSI is more challenging than RSS. Moreover, for applications that do not require fine-grained human-vehicle classification, such as intelligent street lighting systems, RSS-based WHVC has the advantages of easy implementation and low cost. Therefore, investigating the performance of CSI- and RSS-based WHVC methods in different application scenarios could provide valuable insights for the WHVC domain. To address this issue, this article proposes a deep-learning-based WHVC method, which employs deep learning as a tool to evaluate the performance of RSS and CSI methods in various classification tasks. Specifically, this article collects CSI and RSS data for seven different classification tasks in real traffic road scenarios and evaluates these tasks using a convolutional neural network (CNN)-based deep learning model designed in this article. Experimental results demonstrate that for road user categories less than four, RSS-based WHVC achieves higher accuracy than CSI-based WHVC. However, as the number of categories increases, CSI-based WHVC exhibits superior accuracy compared to RSS-based WHVC. Additionally, the developed data set is publicly available at https://github.com/TZ-mx/mixed-dataset.
Keyword:
Wireless sensor networks
Wireless communication
Roads
Feature extraction
Wireless fidelity
Sensors
Transceivers
Channel state information (CSI)
deep learning
human-vehicle classification (HVC)
received signal strength (RSS)
wireless sensing
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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