## 3 Data and Scenarios
### Data Process Procedures  
In the wzone dataset, 29 videos were recorded using DJI Air 2S drones, which feature a 1-inch CMOS sensor with a pixel size of 2.4 \(\upmu\)m. The videos were captured in 5.4 K (5472 \(\times\) 3078) resolution at 30 fps, providing high-definition footage for precise trajectory extraction. The video processing procedure is shown in Figure 1.  
The vehicle detection and tracking procedure is based on OpenVTER, a framework for vehicle trajectory extraction with rotated bounding boxes. The OpenVTER framework consists of five steps: image stabilization, image division, vehicle detection, vehicle tracking, and data post-processing. Due to wind interference during the video capturing process, the footage often drifts and rotates, leading to potential misalignment and distortions in the raw data. To address this, OpenVTER first stabilizes the raw video and then divides each stabilized video frame into overlapping blocks. The rotated bounding boxes are obtained by feeding each block into the extended YOLOX detection model. One of the key reasons we chose the YOLOR model for data processing is its combination of explicit and implicit representation learning. This integration allows YOLOR to extract features directly from the data while also leveraging implicit variables to enhance generalization and detection performance. It not only excels in object detection tasks but also enhances accuracy in handling sparse and complex scenarios. Explicit representation learning involves directly extracting visual features from the input using convolutional neural networks (CNNs). The explicit features can be represented as follows:  
\[Z_{explicit}=f(X;\theta) \tag{1}\]  
where \(X\) is the input image, \(\theta\) represents the parameters of the network, and \(Z_{explicit}\) is the explicit feature vector. Implicit representation learning involves learning latent variables that complement the explicit features, making the model more robust. The implicit features are learned as:  
\[Z_{implicit}=g(W;\lambda) \tag{2}\]  
Figure 1: The process of data extraction. where \(W\) represents the weights of the implicit features and \(\lambda\) represents the parameters for implicit learning. YOLOR combines the explicit and implicit representations to form a more powerful feature set:  
\[Z_{final}=\alpha Z_{explicit}+(1-\alpha)Z_{implicit} \tag{3}\]  
where \(\alpha\) is a hyperparameter that balances explicit and implicit representations. This combination allows YOLOR to perform well across different tasks, maintaining both speed and accuracy, particularly in complex scenarios. Once the vehicles in two adjacent frames are matched, the raw vehicle trajectory is reconstructed. At this stage, the frame ID, pixel coordinates, and vehicle length are calculated for each vehicle. Despite the application of the stabilization algorithm, small translations and rotations may still persist, potentially introducing minor inaccuracies in vehicle tracking and trajectory continuity. These issues can result in small deviations in the final vehicle positions, particularly in areas with continuous environmental disturbances such as wind or lighting changes. To mitigate these potential errors, we applied a symmetric exponential moving average filter (sEMA), a widely used denoising technique, to smooth the geographical coordinates and reduce the effect of minor tracking deviations. The sEMA filter is formulated as follows:  
\[S_{t}=\alpha X_{t}+(1-\alpha)S_{t-1} \tag{4}\]  
where \(S_{t}\) is the smoothed value at time \(t\), \(X_{t}\) is the observed value at time \(t\), and \(\alpha\) is the smoothing factor, \(0<\alpha<1\). The symmetric version of this filter applies smoothing both forwards and backwards over the data. The final smoothed value is the average of the forward and backward smoothed values:  
\[S_{final}(t)=\frac{S_{forward}(t)+S_{backward}(t)}{2} \tag{5}\]  
where \(S_{forward}(t)\) and \(S_{backward}(t)\) are the values obtained by applying the exponential moving average in the forward and backward direction.