# Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
## 3 Design of Framework
### Feature Extraction and Fusion
#### 3.2.1 Spatial and Temporal Feature Extraction  
CNNs, as a biologically inspired model, have a classical structure consisting of convolutional, pooling, and fully connected layers, allowing them to learn sophisticated representations of the input data and extract significant features from them, reflecting the essential patterns in the data. This is why they are frequently utilized and achieve outstanding results in demanding tasks such as object detection [43] and semantic segmentation [44]. AlexNet and VGG16 have both achieved state-of-the-art results in image classification tasks  
Figure 1: Incremental unknown traffic recognition system framework. and have subsequently become popular benchmarks for evaluating new CNN architectures. The two different CNN networks were employed in this study with the network structure shown in Figure 2, and they were used to automatically extract the spatial features of the original traffic from the input images. AlexNet [45] is a seminal deep learning model, introduced in 2012, that earned the top place in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark contest for image classification, in that year. AlexNet uses a relatively simple convolutional and pooling layer structure, which makes the structure and parameters of the network easy to understand and interpret, and the deep network structure in turn makes it excellent for image feature extraction. We trained an AlexNet model without a local response normalization layer in this study. It had 5 convolutional layers and 3 fully connected layers with an input image size of \(32\times 32\times 1\). Each convolutional layer convolves the input with the convolutional kernel and then maps the output features via the activation function. In our model, the filter size of the convolutional layer is \(5\times 5\) pixels, and the step size is 1. The ReLU function is the activation function of the convolutional layer. The first, second, and fifth convolutional layers are connected to the maxpooling layer afterward, which can effectively reduce the number of parameters and complexity of network operation. The filter size of the max pooling layer was designed to be \(3\times 3\) pixels with a stride of 3. The third and fourth convolutional layers are then directly connected. As for the fully connected layers, the first 2 of which have 4096 neurons, and the dropout technique is added to avoid overfitting during training. The third fully connected layer has 1000 neurons, corresponding to the extraction of 1000 feature vectors \(F_{AlexNet}\), where each feature vector has 256 dimensions. Each convolutional layer and fully connected layer is followed by a batch normalization layer to solve the problem of unstable output in deep neural networks. \[F_{AlexNet}=\{F_{1},F_{2},\dots,F_{1000}\} \tag{1}\]  
\[F_{i}=(f_{1},f_{2},\dots,f_{256})^{T},i\in[1,1000] \tag{2}\]  
VGG16 [46] is another popular CNN architecture, consisting of 13 convolutional layers, 5 pooling layers, and 3 fully connected layers. This deep structure allows VGG16 to better capture details and complex features in images, thus improving the accuracy of feature extraction. Like AlexNet, VGG16 also has 5 sets of convolutions, but each set contains more convolutional layers and all convolutional layers use \(3\times 3\) convolution kernels. Each of the first two convolutional sets contains two convolutional layers, and each of the third, fourth, and fifth sets contains three. The difference from AlexNetis that a \(2\times 2\) max pooling is performed after each set of convolutions in VGG16. By continuously using multiple stacked small-sized kernel filters instead of large-sized filters, VGG16 is able to learn more complex features but, at the same time, the multiple nonlinear layers increase the  
Figure 2: Two CNN network structures. The upper part is the network structure of AlexNet, and the lower part is the network structure of VGG16. depth of the network and the number of parameters.