# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 3 Methods
### DLA-Efficient  
DLA (Deep Layer Aggregation) [36] serves as a highly adaptable framework that seamlessly integrates into existing CNN (convolutional neural network) architectures, empowering them for a diverse range of computer vision tasks. CenterNet harnesses the 34-layer DLA-34 convolutional network as its foundational feature extraction backbone. Nevertheless, DLA-34's complexity necessitates substantial computational resources and a considerable number of parameters. To address this, DLA-34 was meticulously crafted upon the foundations of Inception [37] and the Ghost module, aiming to devise an efficient network architecture that mitigates computational demands and enhances the overall efficiency and scalability. The cornerstone innovations in DLA-34 include:  
1. Referring to the combination of a multi-branch structure and depth-separable convolution proposed in Inception for achieving efficient multi-scale feature extraction, we designed a new block module. 2. After the DLA-34 network layer, the MSDA module is introduced, which autonomously learns and identifies the most crucial feature regions to prioritize in images or videos. This capability enables the network to concentrate more on features that profoundly impact the final task, thereby enhancing processing efficiency and performance by prioritizing critical areas. 3. Replacing the ReLU [38] activation function with the smoother Swish [39] activation function enhances the network's accuracy and generalization. The formula is provided below: \[f(x)=x\frac{1}{1+e^{-x}}=x\sigma(x)\] (1)
4. Referring to the GhostNet module, the Ghost module is employed instead of standard 2D convolution to generate an equivalent number of feature maps. The GhostNet module is shown in Figure 3.  
Figure 2: The pipeline of the overall network architecture. The Block-Efficient module is designed to be a simple, lightweight architecture with six convolutional layers. It employs parallel processing paths to extract multiple features from the input, where each submodule specializes in capturing distinct characteristics that are subsequently aggregated along the channel dimension. This design philosophy aims to comprehensively capture diverse characteristics from the input data, thereby enhancing the overall performance. The module efficiently captures local feature information by integrating multiple convolution kernels and pooling operations of varying sizes. Specifically, Conv1 combines the \(1\times 1\) and \(3\times 3\) convolutions to extract and process local features from the input image. Following this, Conv2 incorporates the Ghost module, which significantly reduces the computational requirements and parameters while maintaining the ability to extract multiple feature maps efficiently. Conv3 further enhances performance through the addition of additional convolutional layers. Lastly, Conv4 utilizes pooling and \(1\times 1\) convolutions to extract features, achieving a balance between reducing the computation time and preserving critical information. This architecture is illustrated in Figure 4.  
The pseudo-code flow of the Block-Efficient module is outlined in Algorithm 1. Initially, \(1\times 1\) and \(3\times 3\) convolutions are applied to the input feature map X, resulting in feature maps F1_1 and F1_3, respectively. These two feature maps are then concatenated to form the joint feature map F1. Following this, the Ghost module is utilized on F1 to obtain the feature map F2. Subsequently, two additional \(3\times 3\) convolutional layers are applied to F2, generating the augmented feature map F3. A \(2\times 2\) pooling operation is then performed, followed by a \(1\times 1\) convolution, yielding the feature map F4_1. Finally, all the feature maps (F1, F2, F3, and F4_1) are concatenated to create the final augmented feature map Y, which is returned as the module's output. Figure 4: Schematic diagram of the Block-Efficient structure. Figure 3: Ghost Module diagram.