# A Dynamic Measurement System Based on Adaptive Clustering and Multi-Classifier
## 3 Materials and Methods
### Long-Term Tracking of Dynamic Targets  
Target tracking is a significant area of study within computer vision, boasting extensive applications including autonomous refueling, autonomous landing, and unmanned driving [5]. This field faces major challenges such as occlusion, deformation, background clutter, and scale transformation [6], all of which are particularly prevalent during the aerial refueling process. In target tracking algorithms, the DeepSort algorithm is the most effective when combined with the YOLO algorithm. As an extension of the SORT algorithm (Simple Online and Real-time Tracking), the DeepSort algorithm enhances the accuracy and robustness of target tracking by combining deep learning with traditional tracking techniques. The core of the SORT algorithm lies in its combined use of appearance features and motion features of targets, as well as optimized handling of target matching issues. The algorithm integrates the results of target detection with techniques such as the Hungarian algorithm to achieve continuous tracking of multiple targets. The DeepSort flowchart is shown in Figure 3.  
In real-time target tracking, the extraction of distinct visual features for nearest-neighbor matching notably enhances tracking accuracy under conditions of occlusion and helps mitigate target jumping issues. This article introduces a deep learning model that integrates multi-classifier target detection, monocular estimation data, and state judgment strategies to more effectively address challenges related to deformation and scale transformation. This article employs multi-dimensional and more reliable metrics to replace the correlation metrics used in traditional methods, and it trains the YOLO network on a multi-target drogue dataset to enhance feature extraction and increase network robustness against target loss and occlusion. Traditionally, the correlation between detection results and tracking predictions is addressed using the Hungarian method. The Hungarian algorithm is a method used to calculate the optimal match between detection boxes and prediction boxes. Its goal is to find a method of allocation that minimizes the total cost by continuously searching for augmenting paths. In this study, we also consider the correlation between motion information and monocular depth estimation to tackle the issues of target occlusion and losses in single-view perspectives. The monocular estimation technique, based on deep learning, utilizes relationships between pixel values to infer depth relations. This method involves fitting a function \(f\) to map an image \(I\) to a depth map \(D\). The formula used is as shown in Equation (4):  
\[D=f(I) \tag{4}\]  
Among these, the function \(f\) is primarily developed based on the depth perception capability, and the formula is as in Equation (5):  
\[L=\frac{1}{N}\sum_{i=1}^{N}(\alpha_{D}+\lambda_{D})\cdot l\left(d_{i}d_{i}^{ GT}\right) \tag{5}\]  
Among the variables, \(i\) denotes the pixel index, \(N\) represents the total number of pixels in the depth map, while \(\mathrm{d_{i}}\) and \(\mathrm{d_{i}^{GT}}\) are the predicted and actual depth values, respectively. The term \(l\) is the distance metric and \(\alpha_{D}\) serves as a depth-aware attention factor that directs the network to concentrate more on regions with greater depth values. Consequently, during backpropagation, the gradient focuses intensely on a few distant areas. To prevent gradient vanishing at the start of training and disruptions during the learning of close-range regions, a regularization term is incorporated. This term reflects the learning state throughout the training process. If the depth value predicted by the network closely matches the actual value, \(\lambda_{D}\) tends to 0; otherwise, it approaches 1. This approach ensures that, even in close-range areas (where \(\alpha_{D}\) is near to 0), the gradient can still propagate back through the original loss function, thereby enhancing training stability.