# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 2 Related Work
### Deep Learning Algorithms  
Recently, with the development of UAV computing power and graphics processing unit (GPU) technology, it has become possible to run deep learning models for target tracking on UAV airborne platforms. At present, various target UAV target tracking methods with deep learning opportunities are classified into Siamese neural network-based tracking methods and classification-based convolutional neural network (CNN) target tracking methods. The basic idea behind CNN-based approaches is to divide video frames into background and target regions, thus transforming target tracking into a classification problem; commonly used backbone networks include AlexNet (2017), VGGNet (2018), and ResNet (2019). The multi-domain network (MDNet) algorithm (2018) designs a lightweight CNN structure including three convolutional layers and three fully connected layers to perform a binary classification of the target and background on candidate samples. The core ofMDNet is the introduction of a multi-domain learning strategy; i.e., for each new image sequence, the last fully connected layer of the MDNet model needs to be rebuilt. Although the tracking accuracy of MDNet is high, the requirement to interpret a large number of repeated candidate samples and the fact that the model must be updated online makes the method computationally complex. It is difficult to achieve real-time processing with today's UAV hardware. [PERSON] et al. [21] proposed a tree structure to maintain the multi-CNN model during the tracking process to improve the reliability of the model for the target occlusion or disappearance problem. The score of each candidate region is obtained by weighting the average of the classification numbers of multiple CNNs, which leads to the tracking results. With the rapid development of deep learning, Siamese networks are gradually being applied in the target tracking field, mainly for target position and scale recognition tracking, balancing the contradiction between speed and accuracy. It has gradually become the optimal model for target tracking. In 2017, [PERSON] et al. [22] proposed the Siamese instance (SINT) algorithm to convert the target tracking problem into a similarity metric problem for feature blocks. They proposed training the similarity measure module using neural networks. [PERSON] et al. [23] proposed SiamFC, which no longer trains each module independently, but designs an end-to-end trained network. The authors of [24] imported the Region Proposal Network (RPN) for target detection to Siamese network tracking, transforming the similarity computation problem into a classification and regression problem for targets. DaSiamRPN [25] improves training by adding positive samples and difficult negative samples in the training samples. SiamRPN++ [26] enhances the network's ability to track image edge targets by increasing the positions of the targets in the moving training samples. Although breakthroughs have been made in the above tracking algorithms, most deep learning tracking algorithms use offline training, which cannot quickly adapt to the situation of complete occlusion or even out of view, so it is crucial to explore efficient online training methods for neural networks. The SiamRPN network target tracking algorithm is limited by the depth of the feature extraction network, resulting in poor feature extraction for tiny targets. Using only depth features leads to significant information loss for tiny targets and finally results in the poor tracking accuracy and robustness of the tracking algorithm when facing visible air-to-ground targets. Table 1 outlines the algorithm comparison. To improve the tracking effect, incorporating an attention mechanism into the target tracking model has become an important research direction [27]. [PERSON] et al.