Non-Motorized Lane Target Behavior Classification Based on Millimeter Wave Radar With P-Mrca Convolutional Neural Network

作者：Jiaqing He, Yihan Zhu, Bing Hua, Zhihuo Xu, Yongwei Zhang, Liu Chu, Quan Shi, Robin Braun, Jiajia Shi · 发表于：IEEE Transactions on Biometrics Behavior and Identity Science · 年份：2024 · DOI：10.1109/tbiom.2024.3428577 · 被引用次数：4 · 研究领域：Biometric Identification and Security、Advanced Decision-Making Techniques、Advanced Optical Sensing Technologies

In the fields of road regulation and road safety, the classification of target behaviors for non-motorized lanes is of great significance. However, due to the influence of adverse weather and lighting conditions on the recognition efficiency, we use radar to perform target recognition on non-motorized lanes to cope with the challenges caused by frequent traffic accidents on non-motorized lanes. In this paper, a classification and recognition method for non-motorized lane target behavior is proposed. Firstly, a radar data acquisition system is constructed to extract the micro-Doppler features of the target. Then, in view of the shortcomings of traditional deep learning networks, this paper proposes a multi-scale residual channel attention mechanism that can better perform multi-scale feature extraction and adds it to the convolutional neural network (CNN) model to construct a multi-scale residual channel attention network (MrcaNet), which can identify and classify target behaviors specific to non-motorized lanes. In order to better combine the feature information contained in the high-level features and the low-level features, MrcaNet was combined with the feature pyramid structure, and a more efficient network model feature pyramid-multi-scale residual channel attention network (P-MrcaNet) was designed. The results show that the model has the best scores on classification indexes such as accuracy, precision, recall rate, F1 value and Kappa coefficient, which are about 10% hig...