基于多尺度特征提取的交通模式识别算法

Transportation mode recognition algorithm based on multi-scale feature extraction

针对普适交通模式的场景感知功耗高、场景复杂的问题,提出一种融合残差网络(Res Net)和带孔卷积的交通模式识别算法。首先,使用快速傅里叶变换(FFT)将一维传感器数据转换为二维频谱图像;然后,使用主成分分析(PCA)算法对频谱图像降采样;最后,使用Res Net挖掘交通模式的局部特征,使用带孔卷积挖掘交通模式的全局特征,从而实现对八种交通模式进行识别。与决策树、随机森林、Alex Net等八种算法在实验中的对比评估结果显示,融合Res Net和带孔卷积的交通模式识别算法在静止、走路、跑步等八类交通模式上均有最高准确率。该算法具有良好识别精度和鲁棒性。

Aiming at the problems of high power consumption and complex scene for scene perception in universal transportation modes,a new transportation mode detection algorithm combining Residual Network(ResNet)and dilated convolution was proposed.Firstly,the 1D sensor data was converted into the 2D spectral image by using Fast Fourier Transform(FFT).Then,the Principal Component Analysis(PCA)algorithm was used to realize the downsampling of the spectral image.Finally,the ResNet was used to mine the local features of transportation modes,and the global features of transportation modes were mined with dilated convolution,so as to detect eight transportation modes.Experimental evaluation results show that,compared with 8 algorithms including decision tree,random forest and AlexNet,the transportation mode recognition algorithm combining ResNet and dilated convolution has the highest accuracy in eight traffic patterns including static,walking and running,and the proposed algorithm has good identification accuracy and robustness.