A Novel Method for Human-Vehicle Recognition Based on Wireless Sensing and Deep Learning Technologies

Currently, human-vehicle recognition (HVR) method has been applied in road monitoring, congestion control, and safety protection situations. However, traditional vision-based HVR methods suffer from problems such as high construction cost and low robustness in scenarios with insufficient lighting. For this reason, it is necessary to develop a low-cost and high-robust HVR method for intelligent street light systems (ISLS). A well-designed HVR method can aid the brightness adjustment in ISLSs that operate exclusively at night, facilitating lower power consumption and carbon emission. The paper proposes a novel wireless sensing-based human-vehicle recognition (WsHVR) method based on deep learning technologies, which can be applied in ISLSs that assembled with wireless sensor network (WSN). To solve the problem of limited recognition ability of wireless sensing technology, a deep feature extraction model that combines multi-scale convolution and attention mechanism is proposed, in which the received signal strength (RSS) features of road users are extracted by multi-scale convolution. WsHVR integrates an adaptive registration convolutional attention mechanism (ARCAM) to further feature extraction and classification. The final normalized classification result is obtained by SoftMax function. Experiments show that the proposed WsHVR outperforms existing methods with an accuracy of 99.07%. The dataset and source code related to the paper have been published at https://github.com/TZ-mx/WiParam and https://github.com/TZ-mx/WsHVR, respectively. The proposed WsHVR method has high performance in the field of human-vehicle recognition, potentially providing valuable guidance for the design of intelligent streetlight systems in intelligent transportation systems.